FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Ryter, J
   Fu, XK
   Bhuwalka, K
   Roth, R
   Olivetti, EA
AF Ryter, John
   Fu, Xinkai
   Bhuwalka, Karan
   Roth, Richard
   Olivetti, Elsa A.
TI Emission impacts of China's solid waste import ban and COVID-19 in the
   copper supply chain
SO NATURE COMMUNICATIONS
LA English
DT Article
ID RESOURCES; DEMAND; PRICE; INDICATORS; FOOTPRINT; EPIDEMIC
AB Climate change will increase the frequency and severity of supply chain disruptions and large-scale economic crises, also prompting environmentally protective local policies. Here we use econometric time series analysis, inventory-driven price formation, dynamic material flow analysis, and life cycle assessment to model each copper supply chain actor's response to China's solid waste import ban and the COVID-19 pandemic. We demonstrate that the economic changes associated with China's solid waste import ban increase primary refining within China, offsetting the environmental benefits of decreased copper scrap refining and generating a cumulative increase in CO2-equivalent emissions of up to 13 Mt by 2040. Increasing China's refined copper imports reverses this trend, decreasing CO(2)e emissions in China (up to 180 Mt by 2040) and globally (up to 20 Mt). We test sensitivity to supply chain disruptions using GDP, mining, and refining shocks associated with the COVID-19 pandemic, showing the results translate onto disruption effects. Advanced copper supply chain modeling shows China's new waste trade policy may increase pollution, while limiting other low-value imports reverses this trend. Here the authors show that recycling is vulnerable to supply chain shocks, requiring investment during recoveries to promote a circular economy.
C1 [Ryter, John; Fu, Xinkai; Olivetti, Elsa A.] MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA.
   [Bhuwalka, Karan; Roth, Richard] MIT, Mat Syst Lab, Mat Res Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT); Massachusetts Institute of
   Technology (MIT)
RP Olivetti, EA (corresponding author), MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA.
EM elsao@mit.edu
OI Bhuwalka, Karan/0000-0002-1963-6717; Ryter, John/0000-0002-0343-7553;
   Olivetti, Elsa/0000-0002-8043-2385
FU National Science Foundation [1605050]; CBET program; Directorate For
   Engineering; Div Of Chem, Bioeng, Env, & Transp Sys [1605050] Funding
   Source: National Science Foundation
FX The views expressed in this article are those of the authors alone. The
   authors acknowledge funding from the National Science Foundation Award
   #1605050, CBET program that provided support to make this work possible.
   This research was conducted on the traditional, unceded territory of the
   Wampanoag Nation. We acknowledge the painful history of forced removal
   from this territory, and we respect the many diverse indigenous people
   connected to this land.
CR Aklin M, 2016, ENVIRON RESOUR ECON, V64, P663, DOI 10.1007/s10640-015-9893-1
   AMM Scrap Metal Prices, 2019, AMM SCRAP MET PRIC
   Andreoni V, 2015, INT J EMERG SERV, V4, P6, DOI 10.1108/IJES-09-2014-0012
   [Anonymous], 2020, MININGJOURNAL
   [Anonymous], 2020, SEL DAT
   [Anonymous], 2020, STAND RUL REC BRASS
   [Anonymous], 2018, SCRAP SPEC CIRC
   [Anonymous], 2019, INT EN OUTL 2019
   [Anonymous], 2018, WORLD COPP FACTB 201
   Azapagic A, 2004, J CLEAN PROD, V12, P639, DOI 10.1016/S0959-6526(03)00075-1
   Basov V., 2015, The Chinese Scramble to Mine in Africa"
   Berger M, 2014, ENVIRON SCI TECHNOL, V48, P4521, DOI 10.1021/es404994t
   Brooks AL, 2018, SCI ADV, V4, DOI 10.1126/sciadv.aat0131
   Chen JJ, 2019, RESOUR CONSERV RECY, V146, P35, DOI 10.1016/j.resconrec.2019.03.020
   Ciacci L, 2020, GLOBAL ENVIRON CHANG, V63, DOI 10.1016/j.gloenvcha.2020.102093
   Copper C. B. S., 2020, COVID 19 SUPPLY DISR
   Dong D, 2020, RESOUR CONSERV RECY, V161, DOI 10.1016/j.resconrec.2020.104943
   Eheliyagoda D, 2019, ENVIRON SCI TECHNOL, V53, P13812, DOI 10.1021/acs.est.9b03875
   Elshkaki A, 2016, GLOBAL ENVIRON CHANG, V39, P305, DOI 10.1016/j.gloenvcha.2016.06.006
   Fu X., 2019, THESIS MIT
   Fu XK, 2017, RESOUR CONSERV RECY, V122, P219, DOI 10.1016/j.resconrec.2017.02.012
   Geng Y., 2016, SCIENCE, V6278, P73
   Giurco D., 2006, [No title captured], P20
   Glöser S, 2013, ENVIRON SCI TECHNOL, V47, P6564, DOI 10.1021/es400069b
   Greenstone M, 2018, NY TIMES
   Gregson N, 2019, ENVIRON PLANN A, V51, P1031, DOI 10.1177/0308518X18791175
   Gupta M, 2020, DERMATOL THER, V33, DOI 10.1111/dth.13329
   Gurobi Optimization, 2021, GUROBI OPTIMIZER REF
   Huang Q, 2020, RESOUR CONSERV RECY, V154, DOI 10.1016/j.resconrec.2019.104607
   Hunter JD, 2007, COMPUT SCI ENG, V9, P90, DOI 10.1109/MCSE.2007.55
   ICSG, 2018, ICSG STAT YB, V15, P2018
   International Copper Study Group, 2020, IMP COVID 19 PAND WO
   Ivanov D, 2020, TRANSPORT RES E-LOG, V136, DOI 10.1016/j.tre.2020.101922
   Kaufmann RK, 2009, ENERG ECON, V31, P550, DOI 10.1016/j.eneco.2009.01.013
   Kinch D, 2020, METALS DEMAND OUTPER
   Kirchain R, ESTIMATING COP UNPUB
   LABYS WC, 1971, APPL ECON, V3, P99, DOI 10.1080/00036847100000032
   Laing T, 2020, EXTRACT IND SOC, V7, P580, DOI 10.1016/j.exis.2020.04.003
   Lieder M, 2016, J CLEAN PROD, V115, P36, DOI 10.1016/j.jclepro.2015.12.042
   Liu S, 2021, RESOUR CONSERV RECY, V169, DOI 10.1016/j.resconrec.2021.105525
   MacDonald A., 2020, COVID-19 mining impacts-Impact to mine sites winding down. %3Chttps://
   Maffioli EM, 2020, AM J TROP MED HYG, V102, P924, DOI 10.4269/ajtmh.20-0135
   Mikesell RaymondF., 2013, The World Copper Industry: Structure and Economic Analysis
   Newman L, 2020, COPPER PRICE SURG DU
   Norgate T, 2010, J CLEAN PROD, V18, P266, DOI 10.1016/j.jclepro.2009.09.020
   Norgate T, 2011, MINER ENG, V24, P1563, DOI 10.1016/j.mineng.2011.08.007
   Northey S, 2014, RESOUR CONSERV RECY, V83, P190, DOI 10.1016/j.resconrec.2013.10.005
   Northey S, 2013, J CLEAN PROD, V40, P118, DOI 10.1016/j.jclepro.2012.09.027
   Northey SA, 2017, GLOBAL ENVIRON CHANG, V44, P109, DOI 10.1016/j.gloenvcha.2017.04.004
   Olivetti, J IND ECOL
   Olivetti E. A, EMISSION IMPACTS CHI, DOI [10.6084/m9.figshare.14390489.v3, DOI 10.6084/M9.FIGSHARE.14390489.V3]
   Parker Laura., 2018, National Geographic
   Phoonphongphiphat A., 2018, NIKKEI ASIAN RE 0626
   Qu S, 2019, RESOUR CONSERV RECY, V144, P252, DOI 10.1016/j.resconrec.2019.01.004
   Ray A., 2008, J ENV DEV, V17, P3, DOI [10.1177/1070496507310742, DOI 10.1177/1070496507310742]
   Ren YN, 2020, RESOUR CONSERV RECY, V156, DOI 10.1016/j.resconrec.2020.104699
   Ross S.A., 2013, Handbook of the fundamentals of financial decision making: Part I, P11, DOI DOI 10.1142/9789814417358_0001
   Runge I., 1998, Mining economics and strategy
   S&P Global Market Intelligence, 2019, SNL METALS MINING DA
   S&P Global Market Intelligence, 2019, MIN EC METH MARK INT
   Sappor J, 2020, COVID 19 IMPACTS MET
   Shanghai Metals Market, 2019, CHIN COPP MARK STUD
   Silvapulle P, 1999, J FUTURES MARKETS, V19, P175, DOI 10.1002/(SICI)1096-9934(199904)19:2<175::AID-FUT3>3.3.CO;2-8
   Staub C, 2018, POLICIES DRIVE CHINE
   Staub C, 2018, DETAILS UPCOMING US
   Summers L. H, 1986, INVESTMENT INCENTIVE
   Svedberg P, 2006, WORLD DEV, V34, P501, DOI 10.1016/j.worlddev.2005.07.018
   Sverdrup HU, 2014, RESOUR CONSERV RECY, V87, P158, DOI 10.1016/j.resconrec.2014.03.007
   TURNOVSKY SJ, 1983, ECONOMETRICA, V51, P1363, DOI 10.2307/1912279
   UN Comtrade Database, 2019, UN COMTRADE ONLINE
   Wang JB, 2019, RESOUR CONSERV RECY, V146, P580, DOI 10.1016/j.resconrec.2019.02.008
   Whitaker J, 2011, BASEMAP MATPLOTLIB T
   World Economic Outlook, 2020, WORLD EC OUTL APR 20
   Xia Y., 2018, NYU J INT LAW POLITI, V51, P1101
   Xu J, 2019, TRADE SURPLUS HEAVY
   Zeng X, 2020, NAT COMMUN, V11, DOI 10.1038/s41467-019-14131-z
NR 76
TC 33
Z9 36
U1 3
U2 139
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
EI 2041-1723
J9 NAT COMMUN
JI Nat. Commun.
PD JUN 18
PY 2021
VL 12
IS 1
AR 3753
DI 10.1038/s41467-021-23874-7
PG 13
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics
GA SX2KH
UT WOS:000665038900021
PM 34145227
OA Green Submitted, gold
DA 2026-06-14
ER

PT J
AU Dellink, R
   Lanzi, E
   Chateau, J
AF Dellink, Rob
   Lanzi, Elisa
   Chateau, Jean
TI The Sectoral and Regional Economic Consequences of Climate Change to
   2060
SO ENVIRONMENTAL & RESOURCE ECONOMICS
LA English
DT Article
DE Economic growth; Climate change impacts; Climate change
ID SCENARIO FRAMEWORK; IMPACT; SENSITIVITY; MALARIA; MODEL; VULNERABILITY;
   TEMPERATURE; ADAPTATION; RISKS; COSTS
AB This paper presents a new detailed global quantitative assessment of the economic consequences of climate change (i.e. climate damages) to 2060. The analysis is based on an assessment of a wide range of impacts: changes in crop yields, loss of land and capital due to sea level rise, changes in fisheries catches, capital damages from hurricanes, labour productivity changes and changes in health care expenditures from diseases and heat stress, changes in tourism flows, and changes in energy demand for cooling and heating. A multi-region, multi-sector dynamic computable general equilibrium model is used to link different impacts until 2060 directly to specific drivers of economic growth, including labour productivity, capital stocks and land supply, as well as assess the indirect effects these impacts have on the rest of the economy, and on the economies of other countries. It uses a novel production function approach to identify which aspects of economic activity are directly affected by climate change. The model results show that damages are projected to rise twice as fast as global economic activity; global annual Gross Domestic Product losses are projected to be 1.0-3.3% by 2060. Of the impacts that are modelled, impacts on labour productivity and agriculture are projected to have the largest negative economic consequences. Damages from sea level rise grow most rapidly after the middle of the century. Damages to energy and tourism are very small from a global perspective, as benefits in some regions balance damages in others. Climate-induced damages from hurricanes may have significant effects on local communities, but the macroeconomic consequences are projected to be very small. Net economic consequences are projected to be especially large in Africa and Asia, where the regional economies are vulnerable to a range of different climate impacts. For some countries in higher latitudes, economic benefits can arise from gains in tourism, energy and health. The global assessment also shows that countries that are relatively less affected by climate change may reap trade gains.
C1 [Dellink, Rob; Lanzi, Elisa; Chateau, Jean] OECD Environm Directorate, 2 Rue Andre Pascal, F-75775 Paris, France.
C3 Organisation for Economic Co-operation & Development (OECD)
RP Dellink, R (corresponding author), OECD Environm Directorate, 2 Rue Andre Pascal, F-75775 Paris, France.
EM rob.dellink@oecd.org
OI Dellink, Rob/0000-0002-9892-2278
CR Adger N., 2006, Tyndall centre working paper 90
   [Agrawala S. OECD OECD], 2008, EC ASPECTS ADAPTATIO, DOI [DOI 10.1787/9789264046214-EN, 10.1787/978926404 6214-en]
   Agrawala S, 2011, CLIM CHANG ECON, V2, P175, DOI 10.1142/S2010007811000267
   Akpinar-Ferrand E, 2010, ENVIRON SCI POLICY, V13, P702, DOI 10.1016/j.envsci.2010.09.009
   [Anonymous], GLOB MIT NONCO2 GREE
   [Anonymous], 2013, World Population Prospects: The 2012 Revision, P4
   [Anonymous], WORKING PAPER
   [Anonymous], 2012, GAS EMISSIONS 1990 2
   [Anonymous], 1410 IND STAT I
   [Anonymous], 2014, JRC SCI POLICY REPOR
   [Anonymous], 2013, WORLD EC OUTLOOK DAT
   [Anonymous], EV AGR POL REF US
   [Anonymous], 2014, WORLD EN OUTL 2014, DOI [10.1787/weo-2014-en, DOI 10.1787/WEO-2014-EN]
   [Anonymous], 12866 US INT WORK GR
   [Anonymous], 2011, GARNAUT REV 2011 AUS
   [Anonymous], CLIM CHANG 2014 MIT
   [Anonymous], 2008, MODELLING PRESENT CL
   [Anonymous], 2011, Estimates of the Social Cost of Carbon: Background and Results from the RICE -2011 Model, No. w17540
   [Anonymous], GLOB HLTH STAT
   [Anonymous], 2014, OECD Food, DOI DOI 10.1787/5JXRCLLJNBXQ-EN
   [Anonymous], 2013, OECD Economic Outlook, DOI DOI 10.1787/ECOOUTLOOK-V2013-1-EN
   [Anonymous], OECD FOOD AGR FISHER
   [Anonymous], 2007, EC CLIMATE
   [Anonymous], 2005, Trade and structural adjustment: embracing globalization, DOI DOI 10.1787/9789264064591-EN
   [Anonymous], 2014, Risk Management Magazine
   [Anonymous], NBER NATL BUREAU EC
   [Anonymous], CLIMATECOST PROJECT
   [Anonymous], 2013, Redrawing the energy climate map: World Energy Outlook special report
   [Anonymous], framework development and impacts on TMS functions, DOI 10.1007978-3-319-12457-5
   [Anonymous], 642014 FEEM
   [Anonymous], 2015, CLIMATE CHANGE FOOD
   [Anonymous], 2013, WORLD EN OUTL 2013, DOI DOI 10.1787/WEO-2013-EN
   [Anonymous], 2012, 22012 FEEM
   [Anonymous], 2013, 12866 US INT WORK GR
   [Anonymous], 2015, CLIMATE CHANGE RISKS, DOI DOI 10.1787/9789264234611-EN
   [Anonymous], OECD EC OUTLOOK
   [Anonymous], 2015, The economic consequences of climate change, DOI DOI 10.1787/9789264235410-EN
   Barro R.J., 1995, Economic growth
   Barros V, 2014, CLIMATE CHANGE 2014: IMPACTS, ADAPTATION, AND VULNERABILITY, PT A: GLOBAL AND SECTORAL ASPECTS, pIX
   Berrittella M, 2006, TOURISM MANAGE, V27, P913, DOI 10.1016/j.tourman.2005.05.002
   Bigano A., 2007, Integrated Assessment Journal, V7, P25
   Bigano A, 2008, MITIG ADAPT STRAT GL, V13, P765, DOI 10.1007/s11027-007-9139-9
   Bosello F, 2006, ECOL ECON, V58, P579, DOI 10.1016/j.ecolecon.2005.07.032
   Brown S, 2011, CLIMATECOST PROJECT, V1
   Chateau J., 2011, OECD Environment Working Papers, V41, DOI DOI 10.1787/5KG0NDKJVFHF-EN
   Chateau Jean., 2014, OECD Environment Working Papers, DOI DOI 10.1787/5JZ2QCK2B2VD-EN
   Cheung WWL, 2010, GLOBAL CHANGE BIOL, V16, P24, DOI 10.1111/j.1365-2486.2009.01995.x
   Chima RI, 2003, HEALTH POLICY, V63, P17, DOI 10.1016/S0168-8510(02)00036-2
   Ciscar JC, 2011, P NATL ACAD SCI USA, V108, P2678, DOI 10.1073/pnas.1011612108
   de Bruin KC, 2009, CLIMATIC CHANGE, V95, P63, DOI 10.1007/s10584-008-9535-5
   Dell M, 2009, AM ECON REV, V99, P198, DOI 10.1257/aer.99.2.198
   Dellink R, 2017, GLOBAL ENVIRON CHANG, V42, P200, DOI 10.1016/j.gloenvcha.2015.06.004
   Eboli F, 2010, ENVIRON DEV ECON, V15, P515, DOI 10.1017/S1355770X10000252
   Garnaut R., 2008, GARNAUT CLIMATE CHAN
   Hertel TW, 2010, GLOBAL ENVIRON CHANG, V20, P577, DOI 10.1016/j.gloenvcha.2010.07.001
   Hoogenboom G., 2012, Decision Support System for Agrotechnology Transfer
   Howard PH, 2017, ENVIRON RESOUR ECON, V68, P197, DOI 10.1007/s10640-017-0166-z
   Hsiang S., 2014, The Causal Effect of Environmental Catastrophe on Long-run Economic Growth: Evidence from 6700 Cyclones, DOI DOI 10.3386/W20352
   Hyman RC, 2003, ENVIRON MODEL ASSESS, V8, P175, DOI 10.1023/A:1025576926029
   Johansson Asa, 2013, Long-term growth scenarios, DOI [DOI 10.1787/5K4DDXPR2FMR-EN, 10.1787/5k4ddxpr2fmr-en]
   Johnston R.J., 2020, Role of Oil and Gas Companies in the Energy Transition
   Jones JW, 2003, EUR J AGRON, V18, P235, DOI 10.1016/S1161-0301(02)00107-7
   Kitamori K., 2012, OECD environmental outlook to 2050: the consequences of inaction
   Kjellstrom T, 2009, ARCH ENVIRON OCCUP H, V64, P217, DOI 10.1080/19338240903352776
   KRUGMAN P, 1989, EUR ECON REV, V33, P1031, DOI 10.1016/0014-2921(89)90013-5
   Leakey ADB, 2009, P ROY SOC B-BIOL SCI, V276, P2333, DOI 10.1098/rspb.2008.1517
   Link PM., 2004, Port Econ J, V3, P99, DOI DOI 10.1007/S10258-004-0033-Z
   Lluch C., 1973, EUR ECON REV, V4, P21
   Martens WJM, 1998, ENVIRON HEALTH PERSP, V106, P241, DOI 10.2307/3433924
   Martens WJM, 1997, CLIMATIC CHANGE, V35, P145, DOI 10.1023/A:1005365413932
   MARTENS WJM, 1995, GLOBAL ENVIRON CHANG, V5, P195, DOI 10.1016/0959-3780(95)00051-O
   MARTIN PH, 1995, AMBIO, V24, P200
   Meinshausen M, 2011, ATMOS CHEM PHYS, V11, P1417, DOI 10.5194/acp-11-1417-2011
   Mendelsohn R, 2012, NAT CLIM CHANGE, V2, P205, DOI 10.1038/NCLIMATE1357
   Nakicenvoic N., 2000, Special report on emissions scenarios: A special report of working group iii of the intergovernmental panel on climate change
   Narayanan B., 2012, Global Trade, Assistance, and Production: The GTAP 8 Data Base
   Nelson Gerald C, 2014, Proc Natl Acad Sci U S A, V111, P3274, DOI 10.1073/pnas.1222465110
   Nordhaus W., 1994, MANAGING GLOBAL COMM
   Nordhaus WD, 2010, P NATL ACAD SCI USA, V107, P11721, DOI 10.1073/pnas.1005985107
   Rogelj J, 2012, NAT CLIM CHANGE, V2, P248, DOI [10.1038/nclimate1385, 10.1038/NCLIMATE1385]
   Rosenzweig Cynthia, 2014, Proc Natl Acad Sci U S A, V111, P3268, DOI 10.1073/pnas.1222463110
   Roson Roberto, 2012, International Journal of Sustainable Economy, V4, P270, DOI 10.1504/IJSE.2012.047933
   Schellnhuber HJ, 2014, P NATL ACAD SCI USA, V111, P3225, DOI 10.1073/pnas.1321791111
   Tol R.S. J., 2001, Integrated Assessment, V2, P173, DOI DOI 10.1023/A:1013390516078
   Tol RSJ, 2005, ENVIRON DEV ECON, V10, P615, DOI 10.1017/S1355770X05002354
   Tol RSJ, 2002, ENVIRON RESOUR ECON, V21, P47, DOI 10.1023/A:1014500930521
   Vafeidis AT, 2008, J COASTAL RES, V24, P917, DOI 10.2112/06-0725.1
   van Vuuren DP, 2014, CLIMATIC CHANGE, V122, P373, DOI 10.1007/s10584-013-0906-1
   van Vuuren DP, 2012, GLOBAL ENVIRON CHANG, V22, P21, DOI 10.1016/j.gloenvcha.2011.08.002
   von Lampe M, 2014, AGR ECON-BLACKWELL, V45, P3, DOI 10.1111/agec.12086
   Wing I.Sue., 2014, OECD Environment Working Papers
   Woltjer G. B., 2014, The MAGNET Model: Module Description
   Zivin JG, 2014, J LABOR ECON, V32, P1, DOI 10.1086/671766
   2013, WORLD EC OUTLOOK, P1
NR 94
TC 69
Z9 72
U1 5
U2 97
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0924-6460
EI 1573-1502
J9 ENVIRON RESOUR ECON
JI Environ. Resour. Econ.
PD FEB
PY 2019
VL 72
IS 2
BP 309
EP 363
DI 10.1007/s10640-017-0197-5
PG 55
WC Economics; Environmental Studies
WE Social Science Citation Index (SSCI)
SC Business & Economics; Environmental Sciences & Ecology
GA HK4OM
UT WOS:000457938300001
DA 2026-06-14
ER

PT J
AU Meyer, M
   Hirschnitz-Garbers, M
   Distelkamp, M
AF Meyer, Mark
   Hirschnitz-Garbers, Martin
   Distelkamp, Martin
TI Contemporary Resource Policy and Decoupling TrendsLessons Learnt from
   Integrated Model-Based Assessments
SO SUSTAINABILITY
LA English
DT Article
DE policy assessment; environmental systems thinking and modelling;
   resource policy; material footprint; climate policy; rebound effects;
   simulation; macro-econometric models; multi-region input-output model;
   transformation
ID INTERNATIONAL-TRADE; GLOBAL RESOURCE; INPUT; CONSUMPTION; EXTRACTION;
   CLIMATE; GROWTH; RATES; PRICE
AB Addressing climate change and natural resource depletion has been key to the international and national sustainability agenda for almost 30 years. Despite existing efforts, global CO2 emissions and raw material use levels continue to grow. This seems to suggest the need for more systemic approaches in environmental policy. Our paper contributes modelling results to assess the potential of efficiency improvements to achieve absolute decoupling of global raw material use and environmental impacts from economic growth. We apply the global, dynamic MRIO model GINFORS to simulate potential effects of raw material efficiency improvements in production against a climate mitigation scenario baseline. Our simulation experiments indicate that (rather radical) progress in the raw material efficiency of production technologies in concert with extensive climate mitigation efforts could enable an absolute decoupling of resource use and CO2 emissions from GDP growth at a global level and for some countries. The absolute raw material extraction levels achieved, however, still exceed the material use reduction targets suggested by sustainability scientists. Our findings highlight that achieving such targets without addressing rebound effects is implausible. Hence, we call upon policy makers to integrate rebound mitigation strategies and move beyond exclusively improving efficiency to tackling structural and behavioural changes.
C1 [Meyer, Mark; Distelkamp, Martin] GWS mbH, Global Dev & Resources, D-49080 Osnabruck, Germany.
   [Hirschnitz-Garbers, Martin] Ecol Inst, Resource Conservat & Circular Econ, D-10717 Berlin, Germany.
RP Meyer, M (corresponding author), GWS mbH, Global Dev & Resources, D-49080 Osnabruck, Germany.
EM m.meyer@gws-os.com; martin.hirschnitz-garbers@ecologic.eu;
   distelkamp@gws-os.com
RI Hirschnitz-Garbers, Martin/ABD-3201-2020
OI Meyer, Mark/0000-0001-7637-2925
FU German Environmental Agency; German Environment Ministry [FKZ: 3712 93
   102]
FX The results presented within this paper have been generated by the
   research project "SimRess-Models, potential and long-term scenarios for
   resource efficiency", funded by the German Environmental Agency and the
   German Environment Ministry (FKZ: 3712 93 102).
CR Akenji L, 2016, J CLEAN PROD, V132, P1, DOI 10.1016/j.jclepro.2016.03.071
   Almon C., 1991, ECON SYST RES, V3, P1, DOI [DOI 10.1080/09535319100000001, 10.1080/09535319100000001]
   [Anonymous], 2015, Closing the loop: an EU action plan for the circular economy
   [Anonymous], 2017, Assessing global resource use: A systems approach to resource efficiency and pollution reduction
   [Anonymous], 2015, REPORT INTEGRATED SC
   [Anonymous], TECHNICAL REPORT
   [Anonymous], 2014, COM 398 final
   [Anonymous], 2012, 21 ISSUES 21 CENTURY
   [Anonymous], SUMM PAR AGR
   [Anonymous], 2013, WORLD POP PROSP 2012
   [Anonymous], 2011, Resource revolution: Meeting the world's energy, materials, food, and water needs
   [Anonymous], 2015, The European environment-state and outlook 2015: Synthesis report
   [Anonymous], 2017, Resource Efficiency: Potential and Economic Implications - A Report of the International Resource Panel
   [Anonymous], 2005, EARTHSCAN
   Bahn-Walkowiak B, 2015, RESOURCES-BASEL, V4, P597, DOI 10.3390/resources4030597
   Bardi Ugo., 2011, The Limits to Growth Revisited
   Binswanger M, 2001, ECOL ECON, V36, P119, DOI 10.1016/S0921-8009(00)00214-7
   Bleischwitz R., 2008, Minerals Energy, V23, P84, DOI DOI 10.1080/14041040802247278
   Bleischwitz R, 2017, Sustainable Resource Management, P216
   Boden T.A., 2017, Global, Regional, and National Fossil-Fuel CO2 Emissions, DOI [10.3334/CDIAC/00001_V2017, DOI 10.3334/CDIAC/00001_V2017]
   Bringezu S., 2011, ANN MINES RESPONSABI, P78, DOI [10.3917/re.061.0078, DOI 10.3917/RE.061.0078]
   Bringezu S., 2009, SUSTAINABLE RESOURCE, P155
   Bringezu S, 2015, RESOURCES-BASEL, V4, P25, DOI 10.3390/resources4010025
   Bruckner M, 2012, GLOBAL ENVIRON CHANG, V22, P568, DOI 10.1016/j.gloenvcha.2012.03.011
   Distelkamp M., 2017, LANGFRISTSZENARIEN P, V3
   Distelkamp M, 2019, ECOL ECON, V155, P88, DOI 10.1016/j.ecolecon.2017.07.014
   Dittrich M., 2012, Green Economies Around the World? Implications of Resource Use for Development and the Environment
   EEA (European Environment Agency), 2016, 102016 EEA, DOI [10.2800/240736, DOI 10.2800/240736]
   Ekins P, 2014, ECO-EFFIC IND SCI, V29, P249, DOI 10.1007/978-94-007-5706-6_14
   Ekvall T, 2016, SUSTAINABILITY-BASEL, V8, DOI 10.3390/su8040373
   European Resource Efficiency Platform, 2014, MAN POL REC
   Federal Ministry for the Environment Nature Conservation and Nuclear Safety, 2016, GERM RES EFF PROGR 2
   FischerKowalski M, 2007, ADV ECOL ECON, P1
   GISTEMP Team, GISS SURF TEMP AN GI
   Hatfield-Dodds S, 2017, J CLEAN PROD, V144, P403, DOI 10.1016/j.jclepro.2016.12.170
   Hirschnitz-Garbers M., 2015, FRAMEWORK MEMBER STA
   Hirschnitz-Garbers M, 2016, J CLEAN PROD, V132, P13, DOI 10.1016/j.jclepro.2015.02.038
   Imhof P., 2000, WORKING PAPER, DOI [10.15480/882.114, DOI 10.15480/882.114]
   IRP (International Resource Panel), 2016, RES EFF POT EC IMPL
   Krausmann F, 2009, ECOL ECON, V68, P2696, DOI 10.1016/j.ecolecon.2009.05.007
   Lorenz U., 2017, ECOEFFICIENCY IND SC, V32, P31
   Meadows Donella H, 1972, FUTURE NATURE, P101
   Meyer B, 2019, ECOL ECON, V155, P80, DOI 10.1016/j.ecolecon.2017.06.017
   NOAA (National Oceanic and Atmospheric Administration), 2018, NOAA ESRL ANN CO2 DA
   OECD Working Party on Resource Productivity and Waste & OECD Working Party on Integrating Environmental and Economic Policies, 2017, ENVEPOCWPRPWWPIEEP20
   Pollitt H, 2018, CLIM POLICY, V18, P184, DOI 10.1080/14693062.2016.1277685
   Ripple WJ, 2017, BIOSCIENCE, V67, P1026, DOI 10.1093/biosci/bix125
   Santarius T., 2016, RETHINKING CLIMATE E, P107, DOI [10.1007/978-3-319-38807-6_7, DOI 10.1007/978-3-319-38807-67]
   Schandl H, 2016, J CLEAN PROD, V132, P45, DOI 10.1016/j.jclepro.2015.06.100
   Schmidt-Bleek F., 2008, Sustainability, V4, P1, DOI [DOI 10.1080/15487733.2008.11908009, 10.1080/15487733.2008.11908009]
   Schmidt-Bleek F., 1997, WIEVIEL UMWELT BRAUC
   Sorrell S, 2009, ENERG POLICY, V37, P1356, DOI 10.1016/j.enpol.2008.11.026
   Steffen, 2007, SUSTAINABILITY COLLA, P417
   Steffen W, 2015, SCIENCE, V347, DOI 10.1126/science.1259855
   Sustainable Development Knowledge Platform, 2017, SUST DEV GOAL 12 ENS
   Sustainable Development Knowledge Platform, 2017, SUST DEV GOAL 8 PROM
   Sverdrup H., 2013, J ENV SCI ENG B, VB2, P189
   Sverdrup HU, 2017, J CLEAN PROD, V140, P359, DOI 10.1016/j.jclepro.2015.06.085
   Sverdrup HU, 2016, RESOUR CONSERV RECY, V114, P130, DOI 10.1016/j.resconrec.2016.07.011
   Sverdrup HU, 2014, RESOUR CONSERV RECY, V87, P158, DOI 10.1016/j.resconrec.2014.03.007
   Timmer MP, 2015, REV INT ECON, V23, P575, DOI 10.1111/roie.12178
   Tukker A, 2013, ECON SYST RES, V25, P1, DOI 10.1080/09535314.2012.761179
   Turner GM, 2012, GAIA, V21, P116, DOI 10.14512/gaia.21.2.10
   UNSD, 2014, System of Environmental-Economic Accounting 2012
   Vivanco DF, 2016, ENERG POLICY, V94, P114, DOI 10.1016/j.enpol.2016.03.054
   WBGUGerman Advisory Council on Global Change, 2017, HUM MOV UNL TRANSF P
   Weizsacker E.Lovins., 1997, Factor Four, Doubling Wealth - Halving Resource Consumption
   Weizsacker E.U. von., 2009, FACTOR 5 TRANSFORMIN
   Wiebe KS, 2012, J IND ECOL, V16, P636, DOI 10.1111/j.1530-9290.2012.00504.x
   Wiedmann T, 2013, ECON SYST RES, V25, P143, DOI 10.1080/09535314.2012.761596
   Wiedmann TO, 2015, P NATL ACAD SCI USA, V112, P6271, DOI 10.1073/pnas.1220362110
   Wood R, 2015, SUSTAINABILITY-BASEL, V7, P138, DOI 10.3390/su7010138
NR 72
TC 14
Z9 15
U1 0
U2 10
PU MDPI
PI BASEL
PA ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
SN 2071-1050
J9 SUSTAINABILITY-BASEL
JI Sustainability
PD JUN
PY 2018
VL 10
IS 6
AR 1858
DI 10.3390/su10061858
PG 28
WC Green & Sustainable Science & Technology; Environmental Sciences;
   Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Science & Technology - Other Topics; Environmental Sciences & Ecology
GA GK9LE
UT WOS:000436570100170
OA Green Submitted, gold
DA 2026-06-14
ER

PT J
AU Kim, JB
   Monier, E
   Sohngen, B
   Pitts, GS
   Drapek, R
   McFarland, J
   Ohrel, S
   Cole, J
AF Kim, John B.
   Monier, Erwan
   Sohngen, Brent
   Pitts, G. Stephen
   Drapek, Ray
   McFarland, James
   Ohrel, Sara
   Cole, Jefferson
TI Assessing climate change impacts, benefits of mitigation, and
   uncertainties on major global forest regions under multiple
   socioeconomic and emissions scenarios
SO ENVIRONMENTAL RESEARCH LETTERS
LA English
DT Article
DE MC2; dynamic global vegetation model; climate change; mitigation
   scenarios; uncertainty analysis; forests; wildfire
ID TERRESTRIAL CARBON-CYCLE; NET PRIMARY PRODUCTIVITY; VEGETATION MODELS;
   UNITED-STATES; INTERANNUAL VARIABILITY; TROPICAL FORESTS; LAND-USE;
   FIRE; ECOSYSTEMS; GROSS
AB We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 degrees C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world's forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.
C1 [Kim, John B.; Drapek, Ray] US Forest Serv, Pacific Northwest Res Stn, USDA, 3200 SW Jefferson Way, Corvallis, OR 97330 USA.
   [Monier, Erwan] MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
   [Sohngen, Brent] Ohio State Univ, 2120 Fyffe Rd, Columbus, OH 43210 USA.
   [Pitts, G. Stephen] Oregon State Univ, 3100 SW Jefferson Way, Corvallis, OR 97331 USA.
   [McFarland, James; Ohrel, Sara; Cole, Jefferson] US EPA, 1200 Penn Ave,NW 6207-A, Washington, DC 20460 USA.
C3 United States Department of Agriculture (USDA); United States Forest
   Service; Massachusetts Institute of Technology (MIT); University System
   of Ohio; Ohio State University; Oregon State University; United States
   Environmental Protection Agency
RP Kim, JB (corresponding author), US Forest Serv, Pacific Northwest Res Stn, USDA, 3200 SW Jefferson Way, Corvallis, OR 97330 USA.
EM jbkim@fs.fed.us
RI Kim, John/I-6243-2019; Monier, Erwan/F-6988-2010
OI Kim, John/0000-0002-3720-7916; Sohngen, Brent/0000-0002-9094-160X;
   Monier, Erwan/0000-0001-5533-6570
FU US Environmental Protection Agency [DW-012-92388301, XA-83600001]; US
   Forest Service Western Wildland Environmental Threat Assessment Center;
   US Department of Energy, Office of Biological and Environmental Research
   [DEFG02-94ER61937]
FX The authors wish to acknowledge the financial support of the US
   Environmental Protection Agency (interagency agreement DW-012-92388301).
   John B. Kim is supported in part by the US Forest Service Western
   Wildland Environmental Threat Assessment Center. Erwan Monier is
   supported by the US Environmental Protection Agency under Cooperative
   Agreement #XA-83600001 and by the US Department of Energy, Office of
   Biological and Environmental Research, under grant DEFG02-94ER61937.
CR Andrus RA, 2016, ECOL APPL, V26, P700, DOI 10.1890/15-1121
   [Anonymous], CLIMATIC CHANGE, DOI DOI 10.1007/S10584-011-0101-1
   [Anonymous], 2007, AR4 CLIM CHANG 2007
   [Anonymous], PNWGTR904 USDA FOR S
   [Anonymous], PNWGTR926 USDA FOR S
   [Anonymous], FORESTS CLIMATE CHAN
   [Anonymous], 2013, CLIMATE CHANGE 2013
   [Anonymous], ISLSCP INITIATIVE 2
   [Anonymous], GEOSCI MODEL DEV
   [Anonymous], 5 IPCC
   [Anonymous], 2001, GLOBAL CHANGE BIOL, DOI DOI 10.1046/j.1365-2486.2001.00383.x
   [Anonymous], 2008, BIOL MODELING SIMULA
   [Anonymous], THE CENTURY MODEL
   [Anonymous], ECOSYSTEM MODELING T
   [Anonymous], World Development Indicators
   Bachelet D., 2001, Gen. Tech. Rep. PNW-GTR-508, DOI DOI 10.2737/PNW-GTR-508
   Bachelet D, 2015, GLOBAL CHANGE BIOL, V21, P4548, DOI 10.1111/gcb.13048
   Banerjee M, 1999, CAN J STAT, V27, P3, DOI 10.2307/3315487
   Bartholomé E, 2005, INT J REMOTE SENS, V26, P1959, DOI 10.1080/01431160412331291297
   Beach RH, 2015, ENVIRON RES LETT, V10, DOI 10.1088/1748-9326/10/9/095004
   Brienen RJW, 2015, NATURE, V519, P344, DOI 10.1038/nature14283
   Cramer W, 2004, PHILOS T R SOC B, V359, P331, DOI 10.1098/rstb.2003.1428
   Dale VH, 2001, BIOSCIENCE, V51, P723, DOI 10.1641/0006-3568(2001)051[0723:CCAFD]2.0.CO;2
   Davis MB, 2001, SCIENCE, V292, P673, DOI 10.1126/science.292.5517.673
   Deser C, 2012, NAT CLIM CHANGE, V2, P775, DOI 10.1038/NCLIMATE1562
   Dury M, 2011, IFOREST, V4, P82, DOI 10.3832/ifor0572-004
   Field CB, 1998, SCIENCE, V281, P237, DOI 10.1126/science.281.5374.237
   Fisher JB, 2014, ANNU REV ENV RESOUR, V39, P91, DOI 10.1146/annurev-environ-012913-093456
   Flannigan M, 2009, GLOBAL CHANGE BIOL, V15, P549, DOI 10.1111/j.1365-2486.2008.01660.x
   Food and Agricultural Organization (FAO), 2009, HARM WORLD SOIL DAT
   Fowler HJ, 2007, INT J CLIMATOL, V27, P1547, DOI 10.1002/joc.1556
   Friedlingstein P, 2006, J CLIMATE, V19, P3337, DOI 10.1175/JCLI3800.1
   Friedlingstein P, 2010, NAT GEOSCI, V3, P811, DOI 10.1038/ngeo1022
   Giglio L, 2013, J GEOPHYS RES-BIOGEO, V118, P317, DOI 10.1002/jgrg.20042
   Gonzalez P, 2010, GLOBAL ECOL BIOGEOGR, V19, P755, DOI 10.1111/j.1466-8238.2010.00558.x
   Harris I, 2014, INT J CLIMATOL, V34, P623, DOI 10.1002/joc.3711
   Harvey BJ, 2014, P NATL ACAD SCI USA, V111, P15120, DOI 10.1073/pnas.1411346111
   Hawkins E, 2009, B AM METEOROL SOC, V90, P1095, DOI 10.1175/2009BAMS2607.1
   Heinsch FA, 2006, IEEE T GEOSCI REMOTE, V44, P1908, DOI 10.1109/TGRS.2005.853936
   Hickler T, 2008, GLOBAL CHANGE BIOL, V14, P1531, DOI 10.1111/j.1365-2486.2008.01598.x
   Houghton RA, 2000, GLOBAL ECOL BIOGEOGR, V9, P145, DOI 10.1046/j.1365-2699.2000.00164.x
   Houghton RA, 2000, GLOBAL ECOL BIOGEOGR, V9, P125, DOI 10.1046/j.1365-2699.2000.00166.x
   Hurtt GC, 2011, CLIMATIC CHANGE, V109, P117, DOI 10.1007/s10584-011-0153-2
   Jopp F, 2011, MODELLING COMPLEX ECOLOGICAL DYNAMICS: AN INTRODUCTION INTO ECOLOGICAL MODELLING FOR STUDENTS, TEACHERS & SCIENTISTS, P1, DOI 10.1007/978-3-642-05029-9
   Kicklighter DW, 2014, ENVIRON RES LETT, V9, DOI 10.1088/1748-9326/9/3/035004
   Kicklighter DW, 1999, GLOBAL CHANGE BIOL, V5, P16, DOI 10.1046/j.1365-2486.1999.00003.x
   Kleijnen JackPC., 2008, DESIGN ANAL SIMULATI, V20
   Korner C, 1993, VEGETATION DYNAMICS, P53, DOI DOI 10.1007/978-1-4615-2816-6_3
   Lewis SL, 2009, ANNU REV ECOL EVOL S, V40, P529, DOI 10.1146/annurev.ecolsys.39.110707.173345
   Littell JS, 2011, ECOSPHERE, V2, DOI 10.1890/ES11-00114.1
   Melillo JM, 2009, SCIENCE, V326, P1397, DOI 10.1126/science.1180251
   Mills D, 2015, CLIMATIC CHANGE, V131, P163, DOI 10.1007/s10584-014-1118-z
   Monier E, 2013, GEOSCI MODEL DEV, V6, P2063, DOI 10.5194/gmd-6-2063-2013
   Monier E, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/5/055001
   Monier E, 2015, CLIMATIC CHANGE, V131, P67, DOI 10.1007/s10584-013-1048-1
   Monier E, 2015, CLIMATIC CHANGE, V131, P51, DOI 10.1007/s10584-014-1112-5
   Monier E, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/4/045008
   Mouillot F, 2005, GLOBAL CHANGE BIOL, V11, P398, DOI 10.1111/j.1365-2486.2005.00920.x
   Paltsev S, 2015, CLIMATIC CHANGE, V131, P21, DOI 10.1007/s10584-013-0892-3
   Pan Y, 2006, ECOL APPL, V16, P125, DOI 10.1890/05-0247
   Pan YD, 2011, SCIENCE, V333, P988, DOI 10.1126/science.1201609
   Parisien MA, 2009, ECOL MONOGR, V79, P127, DOI 10.1890/07-1289.1
   Pavlick R, 2013, BIOGEOSCIENCES, V10, P4137, DOI 10.5194/bg-10-4137-2013
   Pearce DW, 2001, ECOSYST HEALTH, V7, P284, DOI 10.1046/j.1526-0992.2001.01037.x
   Piao SL, 2013, GLOBAL CHANGE BIOL, V19, P2117, DOI 10.1111/gcb.12187
   Poulter B, 2014, NATURE, V509, P600, DOI 10.1038/nature13376
   Prentice IC, 2011, NEW PHYTOL, V189, P988, DOI 10.1111/j.1469-8137.2010.03620.x
   Quegan S, 2011, GLOBAL CHANGE BIOL, V17, P351, DOI 10.1111/j.1365-2486.2010.02275.x
   Quillet A, 2010, ENVIRON REV, V18, P333, DOI 10.1139/A10-016
   Randerson JT, 2009, GLOBAL CHANGE BIOL, V15, P2462, DOI 10.1111/j.1365-2486.2009.01912.x
   Reilly J, 2012, ENVIRON SCI TECHNOL, V46, P5672, DOI 10.1021/es2034729
   Riahi K, 2011, CLIMATIC CHANGE, V109, P33, DOI 10.1007/s10584-011-0149-y
   Scheiter S, 2013, NEW PHYTOL, V198, P957, DOI 10.1111/nph.12210
   Shafer SL, 2015, PLOS ONE, V10, DOI 10.1371/journal.pone.0138759
   Sheehan T, 2015, ECOL MODEL, V317, P16, DOI 10.1016/j.ecolmodel.2015.08.023
   Sitch S, 2008, GLOBAL CHANGE BIOL, V14, P2015, DOI 10.1111/j.1365-2486.2008.01626.x
   Sjöström M, 2013, REMOTE SENS ENVIRON, V131, P275, DOI 10.1016/j.rse.2012.12.023
   Sohngen B, 2001, J AGR RESOUR ECON, V26, P326
   Soja AJ, 2007, GLOBAL PLANET CHANGE, V56, P274, DOI 10.1016/j.gloplacha.2006.07.028
   Sokolov AP, 2012, J CLIMATE, V25, P6567, DOI 10.1175/JCLI-D-11-00590.1
   Stocker T., 2014, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, DOI [DOI 10.1017/CBO9781107415324, 10.1017/cbo9781107415324, 10.1017/CBO9781107415324]
   Tang GP, 2010, ECOSPHERE, V1, DOI 10.1890/ES10-00087.1
   Thomson AM, 2011, CLIMATIC CHANGE, V109, P77, DOI 10.1007/s10584-011-0151-4
   Thonicke K, 2001, GLOBAL ECOL BIOGEOGR, V10, P661, DOI 10.1046/j.1466-822X.2001.00175.x
   Tian XH, 2016, ENVIRON RES LETT, V11, DOI 10.1088/1748-9326/11/3/035011
   Turner DP, 2006, REMOTE SENS ENVIRON, V102, P282, DOI 10.1016/j.rse.2006.02.017
   Uusitalo L, 2015, ENVIRON MODELL SOFTW, V63, P24, DOI 10.1016/j.envsoft.2014.09.017
   Waldhoff ST, 2015, CLIMATIC CHANGE, V131, P1, DOI 10.1007/s10584-014-1206-0
   Waring RH, 1998, TREE PHYSIOL, V18, P129
   Zhang FM, 2012, REMOTE SENS ENVIRON, V124, P717, DOI 10.1016/j.rse.2012.06.023
   Zhao MS, 2005, REMOTE SENS ENVIRON, V95, P164, DOI 10.1016/j.rse.2004.12.011
   Zscheischler J, 2014, GLOBAL BIOGEOCHEM CY, V28, P585, DOI 10.1002/2014GB004826
NR 92
TC 48
Z9 51
U1 1
U2 50
PU IOP Publishing Ltd
PI BRISTOL
PA TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
SN 1748-9326
J9 ENVIRON RES LETT
JI Environ. Res. Lett.
PD APR
PY 2017
VL 12
IS 4
AR 045001
DI 10.1088/1748-9326/aa63fc
PG 15
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA EQ1BL
UT WOS:000397804200001
OA Green Submitted, gold
DA 2026-06-14
ER

PT J
AU Battaglini, M
   Harstad, B
AF Battaglini, Marco
   Harstad, Bard
TI Participation and Duration of Environmental Agreements
SO JOURNAL OF POLITICAL ECONOMY
LA English
DT Article
ID PUBLIC-GOODS; INEFFICIENCY; STRATEGIES; PROVISION; CONTRACTS; STABILITY;
   GAME
AB We analyze participation in international environmental agreements in a dynamic game in which countries pollute and invest in green technologies. If complete contracts are feasible, participants eliminate the holdup problemassociated with their investments; however, most countries prefer to free ride rather than participate. If investments are noncontractible, countries face a holdup problemevery time they negotiate; but the free-rider problem can be mitigated and significant participation is feasible. Participation becomes attractive because only large coalitions commit to long-term agreements that circumvent the holdup problem. Under well-specified conditions even the first-best outcome is possible when the contract is incomplete.
C1 [Battaglini, Marco] Cornell Univ, Ithaca, NY 14853 USA.
   [Battaglini, Marco] Einaudi Inst Econ & Finance, Rome, Italy.
   [Harstad, Bard] Univ Oslo, N-0316 Oslo, Norway.
C3 Cornell University; University of Oslo
RP Battaglini, M (corresponding author), Cornell Univ, Ithaca, NY 14853 USA.; Battaglini, M (corresponding author), Einaudi Inst Econ & Finance, Rome, Italy.
OI Battaglini, Marco/0000-0001-9690-0721
FU European Research Council under the European Union's 7th Framework
   Programme [ERC GA 283236]
FX We have benefited from participants at the Climate and the Economy
   conference-Institute for International Economic Studies at Stockholm
   University; the Center for the Study of Industrial Organization/Institut
   d'Economie Industrielle workshop on Industrial Organization-Toulouse;
   the conference Instruments to Curb Global Warming: Recent
   Developments-Paris; the Global Climate Cooperation workshop-St. Gallen;
   the Contracts and Organizations workshop-Norwegian School of Economics;
   the University of Oslo; and the World Bank. We are particularly grateful
   for the comments from Tore Ellingsen,Lucas Maestri, Kjetil Storesletten,
   and Johannes Urpelainen. We thank Nemanja Antic for outstanding research
   assistance. Harstad's research received funding from the European
   Research Council under the European Union's 7th Framework Programme, ERC
   GA 283236.
CR Aldy J.E., 2007, Architectures for Agreement, DOI [10.1017/CBO9780511802027, DOI 10.1017/CBO9780511802027]
   Aldy Joseph., 2009, Post-Kyoto International Climate Policy: Implementing Architectures for Agreement
   [Anonymous], 2011, C PART ITS 16 SESS
   Bagwell K, 2001, Q J ECON, V116, P519, DOI 10.1162/00335530151144096
   Bagwell K, 2010, ANNU REV ECON, V2, P223, DOI 10.1146/annurev.economics.102308.124500
   BARON DP, 1989, AM POLIT SCI REV, V83, P1181, DOI 10.2307/1961664
   Barrett S, 2006, AM ECON REV, V96, P22, DOI 10.1257/000282806777212332
   BARRETT S, 1994, OXFORD ECON PAP, V46, P878, DOI 10.1093/oep/46.Supplement_1.878
   Barrett S., 2002, J I THEORETICAL POLI, V158, P519
   Barrett Scott., 2003, Environment Statecraft
   Battaglini M, 2012, 18585 NBER
   Battaglini M, 2008, AM ECON REV, V98, P201, DOI 10.1257/aer.98.1.201
   Battaglini M, 2007, AM ECON REV, V97, P118, DOI 10.1257/aer.97.1.118
   Battaglini M, 2012, AM POLIT SCI REV, V106, P407, DOI 10.1017/S0003055412000160
   Battaglini Marco, 2015, 482012 PRINC U EC TH
   Beccherle J, 2011, J PUBLIC ECON, V95, P1339, DOI 10.1016/j.jpubeco.2011.04.007
   Bernheim BD, 1998, AM ECON REV, V88, P902
   Besley Timothy., 2011, Pillars of Prosperity: The Political Economics of Development Clusters
   Bosello F, 2003, J EUR ECON ASSOC, V1, P601, DOI 10.1162/154247603322391233
   Breitmeier H., 2006, Analyzing international environmental regimes: From case study to database
   BUCHHOLZ W, 1994, J ECON, V60, P299, DOI 10.1007/BF01227484
   Calvo E, 2012, INT REV ENVIRON RESO, V6, P289, DOI 10.1561/101.00000053
   CARRARO C, 1993, J PUBLIC ECON, V52, P309, DOI 10.1016/0047-2727(93)90037-T
   Carraro C., 2006, The Review of International Organizations, V1, P379, DOI DOI 10.1007/S11558-006-0162-5
   CHWE MSY, 1994, J ECON THEORY, V63, P299, DOI 10.1006/jeth.1994.1044
   DASPREMONT C, 1983, CAN J ECON, V16, P17, DOI 10.2307/134972
   de Zeeuw A, 2008, J ENVIRON ECON MANAG, V55, P163, DOI 10.1016/j.jeem.2007.06.003
   Dixit A, 2000, J PUBLIC ECON, V76, P309, DOI 10.1016/S0047-2727(99)00089-4
   Dutta PK, 2004, P NATL ACAD SCI USA, V101, P5174, DOI 10.1073/pnas.0400489101
   Ellingsen T, 2012, J PUBLIC ECON, V96, P658, DOI 10.1016/j.jpubeco.2012.05.010
   Farrel J., 1989, GAME ECON BEHAV, V1, P327, DOI [DOI 10.1016/0899-8256(89)90021-3, 10.1016/0899-8256(89)90021-3]
   Finus M, 2008, J PUBLIC ECON THEORY, V10, P801, DOI 10.1111/j.1467-9779.2008.00387.x
   GROSSMAN SJ, 1986, J POLIT ECON, V94, P691, DOI 10.1086/261404
   Guriev Sergei, 2005, AM ECON REV, V95, P1269
   Harstad B, 2012, REV ECON STUD, V79, P1527, DOI 10.1093/restud/rds011
   Harstad B, 2012, J POLIT ECON, V120, P77, DOI 10.1086/665405
   Harstad Bard, 2015, COMPLIANCE TEC UNPUB
   Harstad Bard, 2015, J EUROPEAN EC A 0604
   Hart O., 1995, FIRMS CONTRACTS FINA
   Heal Geoffrey, 2011, Tipping climate negotiations, Patent No. w16954
   Helm C, 2015, EUR ECON REV, V75, P112, DOI 10.1016/j.euroecorev.2015.01.007
   Hoel M., 1997, Environmental and Resource Economics, V9, P153, DOI 10.1023/A:1026486715088
   Hoel M., 1992, Environmental and Resource Economics, V2, P141, DOI [10.1007/BF00338240, DOI 10.1007/BF00338240]
   Hoel M, 2010, ENVIRON RESOUR ECON, V47, P395, DOI 10.1007/s10640-010-9384-3
   Hong FH, 2012, J PUBLIC ECON, V96, P685, DOI 10.1016/j.jpubeco.2012.05.003
   Karp L, 2013, J ENVIRON ECON MANAG, V65, P326, DOI 10.1016/j.jeem.2012.09.002
   Kolstad C. D., 2005, Handbook of Environmental Economics, V3, P1562
   LEVHARI D, 1980, BELL J ECON, V11, P322, DOI 10.2307/3003416
   PALFREY TR, 1984, J PUBLIC ECON, V24, P171, DOI 10.1016/0047-2727(84)90023-9
   Peterson M. J., 1997, GLOBAL GOVERNANCE DR
   Ray D, 2001, J POLIT ECON, V109, P1355, DOI 10.1086/323275
   Rubio S.J., 2005, SPAN ECON REV, V7, P89, DOI [DOI 10.1007/s10108-005-0098-6, DOI 10.1007/S10108-005-0098-6, 10.1007/s10108-005-0098-6]
   Rubio SJ, 2007, J ENVIRON ECON MANAG, V54, P296, DOI 10.1016/j.jeem.2007.02.004
   Segal I., 2010, HDB ORG EC
   Tirole J, 1999, ECONOMETRICA, V67, P741, DOI 10.1111/1468-0262.00052
   UNFCCC, 2012, FCCC/CP/2011/9/Add.1
   Van der Ploeg F., 1992, Environmental and Resource Economics, V2, P117, DOI [10.1007/BF00338239, DOI 10.1007/BF00338239]
   Vespa E., 2011, COOPERATION DY UNPUB
   Vincent J.R., 2005, Handbook of Environmental Economics, V3
   Young OR, 2011, P NATL ACAD SCI USA, V108, P19853, DOI 10.1073/pnas.1111690108
NR 60
TC 101
Z9 128
U1 2
U2 53
PU UNIV CHICAGO PRESS
PI CHICAGO
PA 1427 E 60TH ST, CHICAGO, IL 60637-2954 USA
SN 0022-3808
EI 1537-534X
J9 J POLIT ECON
JI J. Polit. Econ.
PD FEB
PY 2016
VL 124
IS 1
BP 160
EP 204
DI 10.1086/684478
PG 45
WC Economics
WE Social Science Citation Index (SSCI)
SC Business & Economics
GA DG2TL
UT WOS:000371920800004
DA 2026-06-14
ER

PT J
AU Wang, Z
   Gu, GX
   Wu, J
   Liu, CX
AF Wang Zheng
   Gu GaoXiang
   Wu Jing
   Liu ChangXin
TI CIECIA: A new climate change integrated assessment model and its
   assessments of global carbon abatement schemes
SO SCIENCE CHINA-EARTH SCIENCES
LA English
DT Article
DE Integrated assessment model; Global cooperating abatement scheme;
   General equilibrium; Process technological progress; Pareto improvement
AB From the perspective of global economic general equilibrium, this study developed a new climate change IAM named CIECIA. The economic core of this IAM is a multi-country-sector general equilibrium model. The endogenous technology progress mode is introduced into CIECIA. Based on this model, three assessment principles of the global cooperating abatement scheme are proposed, including effectiveness, feasibility, and fairness. This study simulated and analyzed six types of primary global cooperating abatement schemes. The simulated results indicate that all of the selected schemes can satisfy the climate mitigation targets by 2100. Thus, they are all effective schemes. However, the schemes have quite different feasibilities and fairness. The Stern Scheme benefits the developed countries, but is unfair to the developing countries. The Nordhaus Scheme promotes the developments of the developing countries. However, it leads to negative impacts on the interests of the developed countries. The principle of convergence on accumulated carbon emissions per capita and the principle of convergence on carbon emissions per capita benefit the economic developments of the middle and low developing countries most. However, these two types of schemes cause tremendous losses to the main economic entities in the world including China. The Pareto Improvement Scheme, which was developed from the Global Economic Growth Scheme, balances the fairness and feasibility in the carbon abatement process and realizes the Pareto improvement of accumulated utilities in all the participating countries. Thus, the Pareto Improvement Scheme is the most reasonable global cooperating carbon abatement scheme.
C1 [Wang Zheng; Wu Jing; Liu ChangXin] China Normal Univ, Inst Policy & Management, Beijing 100080, Peoples R China.
   [Gu GaoXiang] E China Normal Univ, Populat Res Inst, Shanghai 200241, Peoples R China.
   [Wang Zheng; Gu GaoXiang] E China Normal Univ, Minist State Educ China, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China.
C3 Chinese Academy of Sciences; Institutes of Science & Development, CAS;
   East China Normal University; East China Normal University
RP Wang, Z (corresponding author), China Normal Univ, Inst Policy & Management, Beijing 100080, Peoples R China.
EM wangzheng@casipm.ac.cn
RI Wang, Zheng/I-8936-2014; Gu, Gaoxiang/HRD-2647-2023
OI Gu, Gaoxiang/0000-0003-1705-6361
FU National Basic Research Program of China [2012CB955800]
FX This work was supported by the National Basic Research Program of China
   (Grant No. 2012CB955800).
CR Abel AB, 2003, ECONOMETRICA, V71, P551, DOI 10.1111/1468-0262.00417
   Ackerman F, 2009, CLIMATIC CHANGE, V95, P297, DOI 10.1007/s10584-009-9570-x
   Akhtar MK, 2013, ENVIRON MODELL SOFTW, V49, P1, DOI 10.1016/j.envsoft.2013.07.006
   [Anonymous], 2007, HUMAN DEV REPORT
   [Anonymous], 2014, IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I
   [Anonymous], 2005, MIT EMISSIONS PREDIC
   [Anonymous], 2011, World Population Prospects: The 2010 Revision, VCD-ROM
   Bosetti V, 2007, FEEM WORKING PAPER S, V10
   Buchner B, 2005, J POLICY MODEL, V27, P711, DOI 10.1016/j.jpolmod.2005.05.001
   Buonanno P, 2003, RESOUR ENERGY ECON, V25, P11, DOI 10.1016/S0928-7655(02)00015-5
   Chen Wenying, 2005, Journal of Tsinghua University (Science and Technology), V45, P850
   [丁仲礼 Ding Z L], 2009, [中国科学. D辑, 地球科学, Science in China. Series D, Earth Sciences], V39, P1009
   Gu G X, 2014, URBAN ENV STUD, V1, P56
   [何建坤 He Jiankun], 2004, [中国人口·资源与环境, China Population·Resources and Environment], V14, P12
   Jin KY, 2012, AM ECON REV, V102, P2111, DOI 10.1257/aer.102.5.2111
   Kelly D.L., 1999, International yearbook of environmental and resource economics, V2000, P171
   Lee H, 1994, 97 OCED DEV CTR
   Liu C, 2013, THESIS
   Lorentz A, 2008, J EVOL ECON, V18, P389, DOI 10.1007/s00191-008-0096-6
   MANNE A, 1995, ENERG POLICY, V23, P17, DOI 10.1016/0301-4215(95)90763-W
   Narayanan B.G., 2008, Global Trade, Assistance, and Production: The GTAP 7 Data Base
   Nelson R.R., 2009, An evolutionary theory of economic change
   Nordhaus W.D., 2008, QUESTION BALANCE WEI
   Nordhaus WD, 1996, AM ECON REV, V86, P741
   NORDHAUS WD, 1992, SCIENCE, V258, P1315, DOI 10.1126/science.258.5086.1315
   Pizer WA, 1999, RESOUR ENERGY ECON, V21, P255, DOI 10.1016/S0928-7655(99)00005-6
   Rotmans J., 1990, IMAGE: An Integrated Model to Assess the Greenhouse Effect, DOI DOI 10.1007/978-94-009-0691-4
   Scheneider S., 1997, Environmental Modeling and Assessment, V2, P229, DOI DOI 10.1023/A:1019090117643
   Stanton EA, 2009, CLIM DEV, V1, P166, DOI 10.3763/cdev.2009.0015
   Stern N., 2006, STERN REV EC CLIMATE
   Stern N, 2008, AM ECON REV, V98, P1, DOI 10.1257/aer.98.2.1
   Svirezhev Y., 1999, Environmental Modeling & Assessment, V4, P23, DOI 10.1023/A:1019039628546
   Tol RichardS.J., 1997, Environmental Modelling and Assessment, V2, P151, DOI [DOI 10.1023/A:1019017529030, 10.1023/A:1019017529030]
   Tol RSJ, 2002, ENVIRON RESOUR ECON, V21, P47, DOI 10.1023/A:1014500930521
   王铮, 2014, [中国科学. 地球科学, Scientia Sinica Terrae], V44, P1600
   Wang Z, 2012, CHINESE SCI BULL, V57, P4373, DOI 10.1007/s11434-012-5272-2
   [王铮 Wang Zheng], 2006, [生态学报, Acta Ecologica Sinica], V26, P423
   Weitzman ML, 2009, REV ECON STAT, V91, P1, DOI 10.1162/rest.91.1.1
   Weyant J., 1996, Climate change 1995: economic and social dimensions of climate change, P367
   Whitman S, 1997, AM J PUBLIC HEALTH, V87, P1515, DOI 10.2105/AJPH.87.9.1515
   [吴静 Wu Jing], 2014, [生态学报, Acta Ecologica Sinica], V34, P6734
   Zhang Sen., 2012, THESIS
   Zhu Q., 2012, THESIS
NR 43
TC 12
Z9 17
U1 3
U2 28
PU SCIENCE PRESS
PI BEIJING
PA 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA
SN 1674-7313
EI 1869-1897
J9 SCI CHINA EARTH SCI
JI Sci. China-Earth Sci.
PD JAN
PY 2016
VL 59
IS 1
BP 185
EP 206
DI 10.1007/s11430-015-5141-3
PG 22
WC Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology
GA DB0IO
UT WOS:000368192000014
DA 2026-06-14
ER

PT J
AU Schlosser, CA
   Strzepek, K
   Gao, X
   Fant, C
   Blanc, É
   Paltsev, S
   Jacoby, H
   Reilly, J
   Gueneau, A
AF Schlosser, C. Adam
   Strzepek, Kenneth
   Gao, Xiang
   Fant, Charles
   Blanc, Elodie
   Paltsev, Sergey
   Jacoby, Henry
   Reilly, John
   Gueneau, Arthur
TI The future of global water stress: An integrated assessment
SO EARTHS FUTURE
LA English
DT Article
DE Surface-water stress; integrated assessment; water supply; water demand
ID CLIMATE-CHANGE; RISK
AB We assess the ability of global water systems, resolved at 282 assessment subregions (ASRs), to the meet water requirements under integrated projections of socioeconomic growth and climate change. We employ a water resource system (WRS) component embedded within the Massachusetts Institute of Technology Integrated Global System Model (IGSM) framework in a suite of simulations that consider a range of climate policies and regional hydroclimate changes out to 2050. For many developing nations, water demand increases due to population growth and economic activity have a much stronger effect on water stress than climate change. By 2050, economic growth and population change alone can lead to an additional 1.8 billion people living under at least moderate water stress, with 80% of these located in developing countries. Uncertain regional climate change can play a secondary role to either exacerbate or dampen the increase in water stress. The strongest climate impacts on water stress are observed in Africa, but strong impacts also occur over Europe, Southeast Asia, and North America. The combined effects of socioeconomic growth and uncertain climate change lead to a 1.0-1.3 billion increase of the world's 2050 projected population living with overly exploited water conditions-where total potential water requirements will consistently exceed surface water supply. This would imply that adaptive measures would be taken to meet these surface water shortfalls and include: water-use efficiency, reduced and/or redirected consumption, recurrent periods of water emergencies or curtailments, groundwater depletion, additional interbasin transfers, and overdraw from flow intended to maintain environmental requirements.
C1 [Schlosser, C. Adam; Strzepek, Kenneth; Gao, Xiang; Fant, Charles; Blanc, Elodie; Paltsev, Sergey; Jacoby, Henry; Reilly, John] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   [Gueneau, Arthur] Int Food Policy Res Inst, Washington, DC 20036 USA.
C3 Massachusetts Institute of Technology (MIT); CGIAR; International Food
   Policy Research Institute (IFPRI)
RP Schlosser, CA (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM casch@mit.edu
OI Paltsev, Sergey/0000-0003-3287-0732
FU U.S. Department of Energy, Office of Science [DE-FG02-94ER61937,
   DE-FG02-93ER61677, DE-FG02-08ER64597, DE-FG02-06ER64320]; U.S.
   Environmental Protection Agency [XA-83344601-0, XA-83240101,
   XA-83042801-0, PI-83412601-0, RD-83096001, RD-83427901-0]; U.S. National
   Science Foundation [SES-0825915, EFRI-0835414, ATM-0120468, BCS-0410344,
   ATM-0329759, DMS-0426845]; U.S. National Aeronautics and Space
   Administration [NNX07AI49G, NNX08AY59A, NNX06AC30A, NNX09AK26G,
   NNX08AL73G, NNX09AI26G, NNG04GJ80G, NNG04GP30G, NNA06CN09A]; U.S.
   National Oceanic and Atmospheric Administration [DG1330-05-CN-1308,
   NA070AR4310050, NA16GP2290]; U.S. Federal Aviation Administration
   [06-C-NE-MIT]; Electric Power Research Institute [EP-P32616/C15124]
FX The Joint Program on the Science and Policy of Global Change is funded
   by the U.S. Department of Energy, Office of Science under grants
   DE-FG02-94ER61937, DE-FG02-93ER61677, DE-FG02-08ER64597, and
   DE-FG02-06ER64320; the U.S. Environmental Protection Agency under grants
   XA-83344601-0, XA-83240101, XA-83042801-0, PI-83412601-0, RD-83096001,
   and RD-83427901-0; the U.S. National Science Foundation under grants
   SES-0825915, EFRI-0835414, ATM-0120468, BCS-0410344, ATM-0329759, and
   DMS-0426845; the U.S. National Aeronautics and Space Administration
   under grants NNX07AI49G, NNX08AY59A, NNX06AC30A, NNX09AK26G, NNX08AL73G,
   NNX09AI26G, NNG04GJ80G, NNG04GP30G, and NNA06CN09A; the U.S. National
   Oceanic and Atmospheric Administration under grants DG1330-05-CN-1308,
   NA070AR4310050, and NA16GP2290; the U.S. Federal Aviation Administration
   under grant 06-C-NE-MIT; the Electric Power Research Institute under
   grant EP-P32616/C15124; and a consortium of 40 industrial and foundation
   sponsors (for the complete list see
   http://globalchange.mit.edu/sponsors/all). The authors thank Bilhuda
   Rasheed and Tony Smith-Greico for contributions to early versions of the
   manuscript.
CR [Anonymous], 2013, World Population Prospects: The 2012 Revision, P4
   [Anonymous], 2013, FAO Statistical Yearbook 2013 - World Food and Agriculture
   [Anonymous], 2014, UN WORLD WAT DEV REP
   Arnell NW, 2013, J HYDROL, V486, P351, DOI 10.1016/j.jhydrol.2013.02.010
   Blanc E, 2014, EARTHS FUTURE, V2, P197, DOI 10.1002/2013EF000214
   Brown A., 2011, U ARKANSAS THE SUSTA, P21
   Döll P, 2010, HYDROL EARTH SYST SC, V14, P783, DOI 10.5194/hess-14-783-2010
   Fant C., 2012, 214 MIT JPSPCG, V214
   Fung F, 2011, PHILOS T R SOC A, V369, P99, DOI 10.1098/rsta.2010.0293
   Gao X, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/3/035014
   Gosling SN, 2010, PHILOS T R SOC A, V368, P4005, DOI 10.1098/rsta.2010.0164
   Hirabayashi Y, 2008, HYDROLOG SCI J, V53, P754, DOI 10.1623/hysj.53.4.754
   IGFAGCR, 2011, BELM CHALL GLOB ENV
   Meehl GA, 2007, B AM METEOROL SOC, V88, P1383, DOI 10.1175/BAMS-88-9-1383
   Okazaki A, 2012, J METEOROL SOC JPN, V90, P509, DOI 10.2151/jmsj.2012-405
   Oleson K., 2004, NOTE, DOI [10.5065/D6N877R0, DOI 10.5065/D68S4MVH]
   Richels R., 2007, 2 DEP EN OFF BIOL EN
   Rosegrant M., 2008, INT MODEL POLICY ANA
   Schlosser C. A., 2007, 147 MIT JPSPGC
   Schlosser CA, 2013, J CLIMATE, V26, P3394, DOI 10.1175/JCLI-D-11-00730.1
   Siebert S, 2005, HYDROL EARTH SYST SC, V9, P535, DOI 10.5194/hess-9-535-2005
   Siebert S., 2013, PROJECT REPORT
   Smakhtin V., 2005, Taking into Account Environmental Water Requirements in Global-scale Water Resources Assessments
   Sokolov A. P., 2005, 124 MIT JPSPGC
   Strzepek K., 2010, 9 DEV CLIM CHANG WOR
   Strzepek K, 2013, J ADV MODEL EARTH SY, V5, P638, DOI 10.1002/jame.20044
   Tang QH, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2011GL050834
   Thenkabail P.S., 2008, A Global Irrigated Area Map (GIAM) using remote sensing at the end of the last millennium
   Vörösmarty CJ, 2000, SCIENCE, V289, P284, DOI 10.1126/science.289.5477.284
   Wada Y, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009792
   Webster M, 2012, CLIMATIC CHANGE, V112, P569, DOI 10.1007/s10584-011-0260-0
   WILLMOTT CJ, 1992, PROF GEOGR, V44, P84, DOI 10.1111/j.0033-0124.1992.00084.x
   World Commission on Dams (WCOD), 2000, DAMS DEV NEW FRAM DE
NR 33
TC 165
Z9 203
U1 1
U2 62
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
SN 2328-4277
J9 EARTHS FUTURE
JI Earth Future
PD AUG
PY 2014
VL 2
IS 8
BP 341
EP 361
DI 10.1002/2014EF000238
PG 21
WC Environmental Sciences; Geosciences, Multidisciplinary; Meteorology &
   Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Geology; Meteorology & Atmospheric
   Sciences
GA CN0WH
UT WOS:000358134200001
OA Green Submitted, gold
DA 2026-06-14
ER

PT J
AU Prinn, RG
AF Prinn, Ronald G.
TI Development and application of earth system models
SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF
   AMERICA
LA English
DT Article
DE climate change; energy and environment; climate policy
ID CLIMATE-CHANGE; AIR-POLLUTION; UNCERTAINTY; EMISSIONS; POLICY;
   CHEMISTRY; FEEDBACKS; LINKING; OCEAN
AB The global environment is a complex and dynamic system. Earth system modeling is needed to help understand changes in interacting subsystems, elucidate the influence of human activities, and explorepossible future changes. Integrated assessment of environment and human development is arguably the mast difficult and most important "systems" problem faced. To illustrate this approach, we present results from the integrated global system model (IGSM), which consists of coupled submodels addressing economic development, atmospheric chemistry, climate dynamics, and ecosystem processes. An uncertainty analysis implies that without mitigation policies, the global average surface temperature may rise between 3.5 degrees C and 7.4 degrees C from 1981-2000 to 2091-2100(90% confidence limits). Polar temperatures, absent policy, are projected to rise from about 6.4 degrees C to 14 degrees C (90% confidence limits). Similar analysis of four increasingly stringent climate mitigation policy cases involving stabilization of greenhouse gases at various levels indicates that the greatest effect of these policies is to lower the probability of extreme changes. The IGSM is also used to elucidate potential unintended environmental consequences of renewable energy at large scales. There are significant reasons for attention to climate adaptation in addition to climate mitigation that earth system models can help inform. These models can also be applied to evaluate whether "climate engineering" is a viable option or a dangerous diversion. We must prepare young people to address this issue: The problem of preserving a habitable planet will engage present and future generations. Scientists must improve communication if research is to inform the public and policy makers better.
C1 [Prinn, Ronald G.] MIT, Ctr Global Change Sci, Cambridge, MA 02139 USA.
   [Prinn, Ronald G.] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT); Massachusetts Institute of
   Technology (MIT)
RP Prinn, RG (corresponding author), MIT, Ctr Global Change Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM rprinn@mit.edu
OI Prinn, Ronald/0000-0001-5925-3801
FU US Department of Energy; US National Science Foundation; Federal,
   Industrial, and Foundation Sponsors of the MIT Joint Program on the
   Science and Policy of Global Change
FX I thank the two anonymous, reviewers whose comments helped to improve
   the paper significantly. This research was supported by the US
   Department of Energy; US National Science Foundation; and the Federal,
   Industrial, and Foundation Sponsors of the MIT Joint Program on the
   Science and Policy of Global Change.
CR Allen M, 2001, SCIENCE, V293, P430
   [Anonymous], 174 MIT GLOB CHANG J
   [Anonymous], CLIMATE CHANGE GROWI
   [Anonymous], 124 MIT GLOB CHANG J
   [Anonymous], 147 MIT JOINT PROGR
   [Anonymous], CLIMATE CHANGE 2007
   Bonan GB, 2002, J CLIMATE, V15, P3123, DOI 10.1175/1520-0442(2002)015<3123:TLSCOT>2.0.CO;2
   Cohen JB, 2011, ATMOS CHEM PHYS, V11, P7629, DOI 10.5194/acp-11-7629-2011
   Cohen JB, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047417
   Dutkiewicz S, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003405
   Felzer B, 2005, CLIMATIC CHANGE, V73, P345, DOI 10.1007/s10584-005-6776-4
   Forest CE, 2002, SCIENCE, V295, P113, DOI 10.1126/science.1064419
   Forest CE, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL023977
   Forest CE, 2001, CLIM DYNAM, V18, P277, DOI 10.1007/s003820100175
   Forest CE, 2008, TELLUS A, V60, P911, DOI 10.1111/j.1600-0870.2008.00346.x
   Jacoby HD, 1997, ENERG J, V18, P31
   Kamenkovich IV, 2002, CLIM DYNAM, V19, P585, DOI 10.1007/s00382-002-0246-8
   Keith DW, 2004, P NATL ACAD SCI USA, V101, P16115, DOI 10.1073/pnas.0406930101
   Levitus S, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021592
   Matus K, 2008, CLIMATIC CHANGE, V88, P59, DOI 10.1007/s10584-006-9185-4
   Mayer M, 2000, J GEOPHYS RES-ATMOS, V105, P22869, DOI 10.1029/2000JD900307
   Melillo JM, 2009, SCIENCE, V326, P1397, DOI 10.1126/science.1180251
   MELILLO JM, 1993, NATURE, V363, P234, DOI 10.1038/363234a0
   Paltsev S., 2005, 125 MIT GLOB CHANG J
   Paltsev S, 2008, CLIM POLICY, V8, P395, DOI 10.3763/cpol.2007.0437
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Prinn RG, 2004, GEOPHYS MONOGR SER, V150, P297, DOI 10.1029/150GM23
   PRINN RG, 1994, AMBIO, V23, P50
   Reilly J, 1999, NATURE, V401, P549, DOI 10.1038/44069
   Reilly J, 2001, SCIENCE, V293, P430
   Rial JA, 2004, CLIMATIC CHANGE, V65, P11, DOI 10.1023/B:CLIM.0000037493.89489.3f
   Schneider S.H., 1992, Climate System Modeling, P3
   Scott JR, 2008, CLIM DYNAM, V30, P441, DOI 10.1007/s00382-007-0298-x
   Selin NE, 2009, ENVIRON RES LETT, V4, DOI 10.1088/1748-9326/4/4/044014
   Sokolov AP, 2009, J CLIMATE, V22, P5175, DOI 10.1175/2009JCLI2863.1
   Sokolov AP, 2003, J CLIMATE, V16, P1573, DOI 10.1175/1520-0442-16.10.1573
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   Suter G, 2022, INTEGR ENVIRON ASSES, V18, P1117
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   Wang C, 2010, ATMOS CHEM PHYS, V10, P2053, DOI 10.5194/acp-10-2053-2010
   Wang C, 2011, ENVIRON RES LETT, V6, DOI 10.1088/1748-9326/6/2/025101
   Webster M, 2003, CLIMATIC CHANGE, V61, P295, DOI 10.1023/B:CLIM.0000004564.09961.9f
   Webster MD, 2002, ATMOS ENVIRON, V36, P3659, DOI 10.1016/S1352-2310(02)00245-5
   Webster M, 2012, CLIMATIC CHANGE, V112, P569, DOI 10.1007/s10584-011-0260-0
   Xiao X, 1997, TELLUS B, V49, P18, DOI 10.1034/j.1600-0889.49.issue1.2.x
NR 45
TC 51
Z9 60
U1 0
U2 47
PU NATL ACAD SCIENCES
PI WASHINGTON
PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA
SN 0027-8424
EI 1091-6490
J9 P NATL ACAD SCI USA
JI Proc. Natl. Acad. Sci. U. S. A.
PD FEB 26
PY 2013
VL 110
SU 1
BP 3673
EP 3680
DI 10.1073/pnas.1107470109
PG 8
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 102JY
UT WOS:000315842100004
PM 22706645
OA Green Submitted, Bronze
DA 2026-06-14
ER

PT J
AU Monier, E
   Scott, JR
   Sokolov, AP
   Forest, CE
   Schlosser, CA
AF Monier, E.
   Scott, J. R.
   Sokolov, A. P.
   Forest, C. E.
   Schlosser, C. A.
TI An integrated assessment modeling framework for uncertainty studies in
   global and regional climate change: the MIT IGSM-CAM (version 1.0)
SO GEOSCIENTIFIC MODEL DEVELOPMENT
LA English
DT Article
ID EARTH SYSTEM MODELS; QUANTIFYING UNCERTAINTY; HEAT UPTAKE; SENSITIVITY;
   PRECIPITATION; REANALYSIS; TEMPERATURE; CIRCULATION; RECONSTRUCTION;
   CHEMISTRY
AB This paper describes a computationally efficient framework for uncertainty studies in global and regional climate change. In this framework, the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity to a human activity model, is linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Since the MIT IGSM-CAM framework (version 1.0) incorporates a human activity model, it is possible to analyze uncertainties in emissions resulting from both uncertainties in the underlying socio-economic characteristics of the economic model and in the choice of climate-related policies. Another major feature is the flexibility to vary key climate parameters controlling the climate system response to changes in greenhouse gases and aerosols concentrations, e.g., climate sensitivity, ocean heat uptake rate, and strength of the aerosol forcing. The IGSM-CAM is not only able to realistically simulate the present-day mean climate and the observed trends at the global and continental scale, but it also simulates ENSO variability with realistic time scales, seasonality and patterns of SST anomalies, albeit with stronger magnitudes than observed. The IGSM-CAM shares the same general strengths and limitations as the Coupled Model Intercomparison Project Phase 3 (CMIP3) models in simulating present-day annual mean surface temperature and precipitation. Over land, the IGSM-CAM shows similar biases to the NCAR Community Climate System Model (CCSM) version 3, which shares the same atmospheric model. This study also presents 21st century simulations based on two emissions scenarios (unconstrained scenario and stabilization scenario at 660 ppm CO2-equivalent) similar to, respectively, the Representative Concentration Pathways RCP8.5 and RCP4.5 scenarios, and three sets of climate parameters. Results of the simulations with the chosen climate parameters provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century changes in global mean surface air temperature from previous work with the IGSM. Because the IGSM-CAM framework only considers one particular climate model, it cannot be used to assess the structural modeling uncertainty arising from differences in the parameterization suites of climate models. However, comparison of the IGSM-CAM projections with simulations of 31 CMIP5 models under the RCP4.5 and RCP8.5 scenarios show that the range of warming at the continental scale shows very good agreement between the two ensemble simulations, except over Antarctica, where the IGSM-CAM overestimates the warming. This demonstrates that by sampling the climate system response, the IGSM-CAM, even though it relies on one single climate model, can essentially reproduce the range of future continental warming simulated by more than 30 different models. Precipitation changes projected in the IGSM-CAM simulations and the CMIP5 multi-model ensemble both display a large uncertainty at the continental scale. The two ensemble simulations show good agreement over Asia and Europe. However, the ranges of precipitation changes do not overlap - but display similar size - over Africa and South America, two continents where models generally show little agreement in the sign of precipitation changes and where CCSM3 tends to be an outlier.
   Overall, the IGSM-CAM provides an efficient and consistent framework to explore the large uncertainty in future projections of global and regional climate change associated with uncertainty in the climate response and projected emissions.
C1 [Monier, E.; Scott, J. R.; Sokolov, A. P.; Schlosser, C. A.] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   [Forest, C. E.] Penn State Univ, Dept Meteorol, Earth & Environm Syst Inst, University Pk, PA 16802 USA.
C3 Massachusetts Institute of Technology (MIT); Pennsylvania Commonwealth
   System of Higher Education (PCSHE); Pennsylvania State University;
   Pennsylvania State University - University Park
RP Monier, E (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM emonier@mit.edu
RI Forest, Chris/M-1993-2014; Monier, Erwan/F-6988-2010; Sokolov,
   Andrei/JJF-8545-2023
OI Forest, Chris/0000-0002-2643-0186; Monier, Erwan/0000-0001-5533-6570; 
FU US Department of Energy, Office of Science [DE-FG02-94ER61937]; Office
   of Science of the US Department of Energy [DE-AC05-76RL01830]; US
   Department of Energy's Program for Climate Model Diagnosis and
   Intercomparison; Global Organization for Earth System Science Portals
FX This work was funded by the US Department of Energy, Office of Science
   under grants DE-FG02-94ER61937. The Joint Program on the Science and
   Policy of Global Change is funded by a number of federal agencies and a
   consortium of 40 industrial and foundation sponsors. (For the complete
   list see http://globalchange.mit.edu/sponsors/all). This research used
   the Evergreen computing cluster at the Pacific Northwest National
   Laboratory. Evergreen is supported by the Office of Science of the US
   Department of Energy under Contract No. DE-AC05-76RL01830. 20th Century
   Reanalysis V2 data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado,
   USA, from their Web site at http://www.esrl.noaa.gov/psd/. We
   acknowledge the World Climate Research Programme's Working Group on
   Coupled Modelling, which is responsible for CMIP, and we thank the
   climate modeling groups (listed in the Supplement) for producing and
   making available their model output. For CMIP the US Department of
   Energy's Program for Climate Model Diagnosis and Intercomparison
   provides coordinating support and led development of software
   infrastructure in partnership with the Global Organization for Earth
   System Science Portals.
CR Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2
   Andrews T, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051607
   Annan JD, 2011, J CLIMATE, V24, P4529, DOI 10.1175/2011JCLI3873.1
   [Anonymous], 2007, 2 SYNTHESIS ASSESSME, P5
   [Anonymous], 2007, AR4 CLIM CHANG 2007
   [Anonymous], CLIMATIC CH IN PRESS
   [Anonymous], CLIMATE CHANGE 2007
   [Anonymous], 125 MIT JPSPGC
   [Anonymous], J GEOPHYS RES ATMOS
   [Anonymous], 122 MIT JPSPGC
   Balmaseda MA, 2013, GEOPHYS RES LETT, V40, P1754, DOI 10.1002/grl.50382
   Brovkin V, 2006, CLIM DYNAM, V26, P587, DOI 10.1007/s00382-005-0092-6
   Collins M, 2006, CLIM DYNAM, V27, P127, DOI 10.1007/s00382-006-0121-0
   Collins W. D., 2004, DESCRIPTION NCAR COM
   Collins WD, 2006, J CLIMATE, V19, P2122, DOI 10.1175/JCLI3761.1
   Compo GP, 2011, Q J ROY METEOR SOC, V137, P1, DOI 10.1002/qj.776
   Dalan F, 2005, J CLIMATE, V18, P2482, DOI 10.1175/JCLI3412.1
   Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828
   Deser C, 2012, NAT CLIM CHANGE, V2, P775, DOI 10.1038/NCLIMATE1562
   Deser C, 2012, CLIM DYNAM, V38, P527, DOI 10.1007/s00382-010-0977-x
   Dickinson RE, 2006, J CLIMATE, V19, P2302, DOI 10.1175/JCLI3742.1
   Dutkiewicz S, 2009, GLOBAL BIOGEOCHEM CY, V23, DOI 10.1029/2008GB003405
   Eby M, 2013, CLIM PAST, V9, P1111, DOI 10.5194/cp-9-1111-2013
   Forest CE, 2001, CLIM DYNAM, V18, P277, DOI 10.1007/s003820100175
   Forest CE, 2008, TELLUS A, V60, P911, DOI 10.1111/j.1600-0870.2008.00346.x
   Forster P, 2007, AR4 CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P129
   García-Herrera R, 2010, CRIT REV ENV SCI TEC, V40, P267, DOI 10.1080/10643380802238137
   Gleckler PJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008972
   Gregory JM, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023209
   HANSEN J, 1983, MON WEATHER REV, V111, P609, DOI 10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2
   Hawkins E, 2011, WEATHER, V66, P175, DOI 10.1002/wea.761
   Horowitz LW, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002853
   Hurrell JW, 2008, J CLIMATE, V21, P5145, DOI 10.1175/2008JCLI2292.1
   Hurrell JW, 2006, J CLIMATE, V19, P2162, DOI 10.1175/JCLI3762.1
   Jones PD, 1999, REV GEOPHYS, V37, P173, DOI 10.1029/1999RG900002
   Kalnay E, 1996, B AM METEOROL SOC, V77, P437, DOI 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
   Liu Y, 1996, THESIS MIT
   Marshall J, 1997, J GEOPHYS RES-OCEANS, V102, P5733, DOI 10.1029/96JC02776
   Mayer M, 2000, J GEOPHYS RES-ATMOS, V105, P22869, DOI 10.1029/2000JD900307
   Meehl G. A., CLIMATE CHANGE 2007
   Meehl GA, 2007, B AM METEOROL SOC, V88, P1383, DOI 10.1175/BAMS-88-9-1383
   Meehl GA, 2013, J CLIMATE, V26, P7298, DOI 10.1175/JCLI-D-12-00548.1
   MELILLO JM, 1993, NATURE, V363, P234, DOI 10.1038/363234a0
   Monier E, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/4/045008
   Morice CP, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017187
   Moss RH, 2010, NATURE, V463, P747, DOI 10.1038/nature08823
   Murphy JM, 2004, NATURE, V430, P768, DOI 10.1038/nature02771
   Nakicenvoic N., 2000, Special report on emissions scenarios: A special report of working group iii of the intergovernmental panel on climate change
   Oleson K., 2004, NOTE, DOI [10.5065/D6N877R0, DOI 10.5065/D68S4MVH]
   Petoukhov V, 2005, CLIM DYNAM, V25, P363, DOI 10.1007/s00382-005-0042-3
   Plattner GK, 2008, J CLIMATE, V21, P2721, DOI 10.1175/2007JCLI1905.1
   Raper SCB, 2002, J CLIMATE, V15, P124, DOI 10.1175/1520-0442(2002)015<0124:TROCSA>2.0.CO;2
   Rayner NA, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002670
   Reilly J, 2001, SCIENCE, V293, P430
   Reilly J, 2013, CLIMATIC CHANGE, V117, P561, DOI 10.1007/s10584-012-0635-x
   Robine JM, 2008, CR BIOL, V331, P171, DOI 10.1016/j.crvi.2007.12.001
   Schlosser C. A., 2007, 147 MIT JPSPGC
   Schlosser CA, 2013, J CLIMATE, V26, P3394, DOI 10.1175/JCLI-D-11-00730.1
   Scott JR, 2008, CLIM DYNAM, V30, P441, DOI 10.1007/s00382-007-0298-x
   SENIOR CA, 1993, J CLIMATE, V6, P393, DOI 10.1175/1520-0442(1993)006<0393:CDACTI>2.0.CO;2
   Smith TM, 2013, J ATMOS OCEAN TECH, V30, P1107, DOI 10.1175/JTECH-D-12-00197.1
   Smith TM, 2012, J ATMOS OCEAN TECH, V29, P1505, DOI 10.1175/JTECH-D-12-00001.1
   Sokolov AP, 2009, J CLIMATE, V22, P5175, DOI 10.1175/2009JCLI2863.1
   Sokolov A. P., 2005, 124 MIT JPSPGC
   Sokolov AP, 2006, J CLIMATE, V19, P3294, DOI 10.1175/JCLI3791.1
   Sokolov AP, 2012, J CLIMATE, V25, P6567, DOI 10.1175/JCLI-D-11-00590.1
   Sokolov AP, 2003, J CLIMATE, V16, P1573, DOI 10.1175/1520-0442-16.10.1573
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   Stainforth DA, 2005, NATURE, V433, P403, DOI 10.1038/nature03301
   Stouffer RJ, 2006, J CLIMATE, V19, P1365, DOI 10.1175/JCLI3689.1
   Taylor KE, 2012, B AM METEOROL SOC, V93, P485, DOI 10.1175/BAMS-D-11-00094.1
   Thompson DWJ, 2002, SCIENCE, V296, P895, DOI 10.1126/science.1069270
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   Webster MD, 2000, CLIMATIC CHANGE, V46, P417, DOI 10.1023/A:1005685317358
   Webster MD, 2008, 165 MIT JPSPGC
   Webster M, 2012, CLIMATIC CHANGE, V112, P569, DOI 10.1007/s10584-011-0260-0
   Xie PP, 1997, B AM METEOROL SOC, V78, P2539, DOI 10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2
   Yokohata T, 2010, J CLIMATE, V23, P1392, DOI 10.1175/2009JCLI2917.1
   Yu JY, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL044082
   Zickfeld K, 2013, J CLIMATE, V26, P5782, DOI 10.1175/JCLI-D-12-00584.1
NR 80
TC 47
Z9 51
U1 0
U2 25
PU COPERNICUS GESELLSCHAFT MBH
PI GOTTINGEN
PA BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY
SN 1991-959X
EI 1991-9603
J9 GEOSCI MODEL DEV
JI Geosci. Model Dev.
PY 2013
VL 6
IS 6
BP 2063
EP 2085
DI 10.5194/gmd-6-2063-2013
PG 23
WC Geosciences, Multidisciplinary
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Geology
GA 280SG
UT WOS:000329050500013
OA Green Submitted, gold
DA 2026-06-14
ER

PT J
AU Reilly, J
   Melillo, J
   Cai, YX
   Kicklighter, D
   Gurgel, A
   Paltsev, S
   Cronin, T
   Sokolov, A
   Schlosser, A
AF Reilly, John
   Melillo, Jerry
   Cai, Yongxia
   Kicklighter, David
   Gurgel, Angelo
   Paltsev, Sergey
   Cronin, Timothy
   Sokolov, Andrei
   Schlosser, Adam
TI Using Land To Mitigate Climate Change: Hitting the Target, Recognizing
   the Trade-offs
SO ENVIRONMENTAL SCIENCE & TECHNOLOGY
LA English
DT Article
ID CARBON-SEQUESTRATION; BIOFUELS; FORESTS; CROPS; CO2
AB Land can be used in several ways to mitigate climate change, but especially under changing environmental conditions there may be implications for food prices. Using an integrated global system model, We explore the roles that these land-use options can play in a global mitigation strategy to stabilize Earth's average temperature within 2 degrees C of the preindustrial level and their impacts on agriculture. We show that an ambitious global Energy-Only climate policy that includes biofuels would likely not achieve the 2 degrees C target. A thought-experiment where the world ideally prices carbon fluxes combined with biofuels (Energy-Land policy) gets the world much closer Land could become a large net carbon sink Of about 178 Pg C over The 21st century with price incentives in the Energy-Land scenario. With,land carbon pricing but without biofuels (a No-Biofuel scenario) the carbon sink is nearly identical, to the case with biofuels, but emissions from energy are somewhat higher, thereby results in more warming. Absent such incentives, land is either a much smaller net carbon sink (+37 Pg C - Energy-Only policy) or a net source (-21 Pg C - No-Policy). The significant trade-off with this integrated land-use approach is that prices for agricultural products rise substantially because of mitigation costs borne by the sector and higher land prices. Share of income spent on food for wealthier regions continues to fall, but for the poorest regions, higher food prices lead to a rising share of income spent on food.
C1 [Reilly, John; Cai, Yongxia; Gurgel, Angelo; Paltsev, Sergey; Cronin, Timothy; Sokolov, Andrei; Schlosser, Adam] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   [Melillo, Jerry; Kicklighter, David] Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA.
   [Gurgel, Angelo] Fundacao Getulio Vargas, Sao Paulo Sch Econ, Sao Paulo, Brazil.
C3 Massachusetts Institute of Technology (MIT); Marine Biological
   Laboratory - Woods Hole; Getulio Vargas Foundation
RP Reilly, J (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave,MIT E19-411, Cambridge, MA 02139 USA.
EM jreilly@mit.edu
RI Sokolov, Andrei/JJF-8545-2023; Gurgel, Angelo/D-9446-2013
OI Gurgel, Angelo/0000-0001-8331-9508; Paltsev, Sergey/0000-0003-3287-0732;
   Cai, Yongxia/0000-0002-1954-269X
FU U.S. Department of Energy, Office of Science [DE-FG02-94ER61937,
   DE-FG02-06ER64320]; U.S. Environmental Protection Agency [XA-83344601-0,
   RD-83427901-0]; U.S. National Science Foundation [SES-0825915,
   DMS-0426845]; U.S. National Aeronautics and Space Administration
   [NNX07AI49G, NNA06CN09A]; U.S. National Oceanic and Atmospheric
   Administration [DG1330-05-CN-1308, NA16GP2290]; U.S. Federal Aviation
   Administration; Electric Power Research Institute; consortium of 40
   industrial and foundation sponsors; David and Lucile Packard Foundation;
   Office of Science (BER), U.S. Department of Energy [DE-FG02-08ER64648];
   Brazilian National Council for Scientific and Technological Development
   (CNPq); Div Of Information & Intelligent Systems; Direct For Computer &
   Info Scie & Enginr [1028163] Funding Source: National Science Foundation
FX The Joint Program on the Science and Policy of Global Change is funded
   by the U.S. Department of Energy, Office of Science under grants
   DE-FG02-94ER61937 and DE-FG02-06ER64320; the U.S. Environmental
   Protection Agency under grants XA-83344601-0 and RD-83427901-0; the U.S.
   National Science Foundation under grants SES-0825915 and DMS-0426845;
   the U.S. National Aeronautics and Space Administration under grants
   NNX07AI49G and NNA06CN09A; the U.S. National Oceanic and Atmospheric
   Administration under grants DG1330-05-CN-1308 and NA16GP2290; the U.S.
   Federal Aviation Administration; the Electric Power Research Institute;
   and a consortium of 40 industrial and foundation sponsors
   (http://globalchange.mit.edu/sponsors/current.html). This research is
   also supported by grants to the MBL from the David and Lucile Packard
   Foundation and the Office of Science (BER), U.S. Department of Energy
   Grant No. DE-FG02-08ER64648, and financial support from the Brazilian
   National Council for Scientific and Technological Development (CNPq).
CR Alig R, 2010, FOREST POLICY ECON, V12, P67, DOI 10.1016/j.forpol.2009.09.012
   Anderson-Teixeira KJ, 2009, GCB BIOENERGY, V1, P75, DOI 10.1111/j.1757-1707.2008.01001.x
   [Anonymous], EC ANAL LAND USE GLO
   [Anonymous], WORLD GREENH GAS EM
   [Anonymous], GLOBAL TIMBER MARKET
   [Anonymous], JOINT PROGR REP SER
   [Anonymous], WORLD POP PROSP 2008
   [Anonymous], AVOIDING DANGEROUS C
   [Anonymous], THESIS MIT CAMBRIDGE
   [Anonymous], J AGR FOOD IND ORG
   Blanco-Canqui H, 2010, AGRON J, V102, P403, DOI 10.2134/agronj2009.0333
   Canadell JG, 2008, SCIENCE, V320, P1456, DOI 10.1126/science.1155458
   Dimaranan B.V., 2002, GLOBAL TRADE ASSISTA
   Felzer B, 2005, CLIMATIC CHANGE, V73, P345, DOI 10.1007/s10584-005-6776-4
   Felzer B, 2004, TELLUS B, V56, P230, DOI 10.1111/j.1600-0889.2004.00097.x
   Hurtt GC, 2006, GLOBAL CHANGE BIOL, V12, P1208, DOI 10.1111/j.1365-2486.2006.01150.x
   McGuire AD, 2001, GLOBAL BIOGEOCHEM CY, V15, P183, DOI 10.1029/2000GB001298
   Meehl GA, 2007, AR4 CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P747
   Meinshausen M, 2009, NATURE, V458, P1158, DOI 10.1038/nature08017
   Melillo JM, 2009, SCIENCE, V326, P1397, DOI 10.1126/science.1180251
   NILSSON S, 1995, CLIMATIC CHANGE, V30, P267, DOI 10.1007/BF01091928
   [Portner H.-O. IPCC IPCC], 2019, Climate change 2022-Impacts, adaptation and vulnerability, DOI [DOI 10.1017/9781009325844, DOI 10.1017/9781009325844.008]
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Reilly J, 2007, ENERG POLICY, V35, P5370, DOI 10.1016/j.enpol.2006.01.040
   Reilly J, 2007, GREENHOUSE GAS SINKS, P115, DOI 10.1079/9781845931896.0115
   Riahi K, 2007, TECHNOL FORECAST SOC, V74, P887, DOI 10.1016/j.techfore.2006.05.026
   Richards KR, 2004, CLIMATIC CHANGE, V63, P1, DOI 10.1023/B:CLIM.0000018503.10080.89
   Righelato R, 2007, SCIENCE, V317, P902, DOI 10.1126/science.1141361
   Schenk PM, 2008, BIOENERG RES, V1, P20, DOI 10.1007/s12155-008-9008-8
   Tian HQ, 2003, GLOBAL PLANET CHANGE, V37, P201, DOI 10.1016/S0921-8181(02)00205-9
   Tilman D, 2006, SCIENCE, V314, P1598, DOI 10.1126/science.1133306
   van Minnen Jelle G, 2008, Carbon Balance Manag, V3, P3, DOI 10.1186/1750-0680-3-3
   Wise M, 2009, SCIENCE, V324, P1183, DOI 10.1126/science.1168475
NR 33
TC 93
Z9 107
U1 0
U2 73
PU AMER CHEMICAL SOC
PI WASHINGTON
PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA
SN 0013-936X
EI 1520-5851
J9 ENVIRON SCI TECHNOL
JI Environ. Sci. Technol.
PD JUN 5
PY 2012
VL 46
IS 11
BP 5672
EP 5679
DI 10.1021/es2034729
PG 8
WC Engineering, Environmental; Environmental Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Engineering; Environmental Sciences & Ecology
GA 952HJ
UT WOS:000304783000009
PM 22533690
DA 2026-06-14
ER

PT J
AU Webster, M
   Sokolov, AP
   Reilly, JM
   Forest, CE
   Paltsev, S
   Schlosser, A
   Wang, C
   Kicklighter, D
   Sarofim, M
   Melillo, J
   Prinn, RG
   Jacoby, HD
AF Webster, Mort
   Sokolov, Andrei P.
   Reilly, John M.
   Forest, Chris E.
   Paltsev, Sergey
   Schlosser, Adam
   Wang, Chien
   Kicklighter, David
   Sarofim, Marcus
   Melillo, Jerry
   Prinn, Ronald G.
   Jacoby, Henry D.
TI Analysis of climate policy targets under uncertainty
SO CLIMATIC CHANGE
LA English
DT Article
ID FORECAST
AB Although policymaking in response to the climate change threat is essentially a challenge of risk management, most studies of the relation of emissions targets to desired climate outcomes are either deterministic or subject to a limited representation of the underlying uncertainties. Monte Carlo simulation, applied to the MIT Integrated Global System Model (an integrated economic and earth system model of intermediate complexity), is used to analyze the uncertain outcomes that flow from a set of century-scale emissions paths developed originally for a study by the U.S. Climate Change Science Program. The resulting uncertainty in temperature change and other impacts under these targets is used to illustrate three insights not obtainable from deterministic analyses: that the reduction of extreme temperature changes under emissions constraints is greater than the reduction in the median reduction; that the incremental gain from tighter constraints is not linear and depends on the target to be avoided; and that comparing median results across models can greatly understate the uncertainty in any single model.
C1 [Webster, Mort; Sokolov, Andrei P.; Reilly, John M.; Paltsev, Sergey; Schlosser, Adam; Wang, Chien; Prinn, Ronald G.; Jacoby, Henry D.] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   [Webster, Mort] MIT, Engn Syst Div, Cambridge, MA 02139 USA.
   [Forest, Chris E.] Penn State Univ, Dept Meteorol, University Pk, PA 16802 USA.
   [Kicklighter, David; Melillo, Jerry] Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA.
   [Sarofim, Marcus] US EPA, Washington, DC 20460 USA.
C3 Massachusetts Institute of Technology (MIT); Massachusetts Institute of
   Technology (MIT); Pennsylvania Commonwealth System of Higher Education
   (PCSHE); Pennsylvania State University; Pennsylvania State University -
   University Park; Marine Biological Laboratory - Woods Hole; United
   States Environmental Protection Agency
RP Webster, M (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM mort@MIT.EDU
RI ; Sokolov, Andrei/JJF-8545-2023; Forest, Chris/M-1993-2014
OI Prinn, Ronald/0000-0001-5925-3801; Paltsev, Sergey/0000-0003-3287-0732;
   Sarofim, Marcus/0000-0001-7753-1676; Forest, Chris/0000-0002-2643-0186
FU U.S. Department of Energy [DE-FG02-94ER61937]; U.S. Environmental
   Protection Agency; U.S. National Science Foundation; U.S. National
   Aeronautics and Space Administration; U.S. National Oceanographic and
   Atmospheric Administration; MIT Joint Program on the Science and Policy
   of Global Change; Direct For Computer & Info Scie & Enginr; Div Of
   Information & Intelligent Systems [1027955] Funding Source: National
   Science Foundation; U.S. Department of Energy (DOE) [DE-FG02-94ER61937]
   Funding Source: U.S. Department of Energy (DOE)
FX We thank the three anonymous referees for helpful comments on this
   manuscript. This analysis and the development of the IGSM model used
   here was supported by the U.S. Department of Energy (DE-FG02-94ER61937),
   U.S. Environmental Protection Agency, U.S. National Science Foundation,
   U.S. National Aeronautics and Space Administration, U.S. National
   Oceanographic and Atmospheric Administration and the Industry and
   Foundation Sponsors of the MIT Joint Program on the Science and Policy
   of Global Change.
CR [Anonymous], FCCCCP20076 UN
   [Anonymous], 14832 NBER
   [Anonymous], INT LEG MAT
   [Anonymous], SCENARIOS GREENHOUSE
   [Anonymous], 2007, CLIMATE CHANGE 2007
   [Anonymous], CLIM CHANG 2007
   [Anonymous], 174 MIT JPSPGS
   [Anonymous], IPCC EXP M REP 19 21
   [Anonymous], G8 SUMM CHAIRS SUMM
   [Anonymous], SIMULATION MONTE CAR
   Canadell JG, 2007, P NATL ACAD SCI USA, V104, P18866, DOI 10.1073/pnas.0702737104
   HANSEN J, 1988, J GEOPHYS RES-ATMOS, V93, P9341, DOI 10.1029/JD093iD08p09341
   Knutti R, 2008, J CLIMATE, V21, P2651, DOI 10.1175/2007JCLI2119.1
   Kolstad CD, 1996, J ENVIRON ECON MANAG, V31, P1, DOI 10.1006/jeem.1996.0028
   Meehl GA, 2007, AR4 CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P747
   Moss R., [No title captured]
   Nakicenvoic N., 2000, Special report on emissions scenarios: A special report of working group iii of the intergovernmental panel on climate change
   Pielke R, 2008, NATURE, V452, P531, DOI 10.1038/452531a
   Prinn R, 2011, CLIMATIC CHANGE, V104, P515, DOI [10.1007/s10584-009-9792-y, 10.1007/s10584-009-9792-v]
   Ramaswamy VO., 2001, Climate Change 2001: the Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, P881
   Schneider SH, 2007, AR4 CLIMATE CHANGE 2007: IMPACTS, ADAPTATION, AND VULNERABILITY, P779
   Sokolov AP, 2009, J CLIMATE, V22, P5175, DOI 10.1175/2009JCLI2863.1
   Sokolov A. P., 2005, 124 MIT JPSPGC
   Sokolov AP, 2008, J CLIMATE, V21, P3776, DOI 10.1175/2008JCLI2038.1
   Webster MD, 2008, 165 MIT JPSPGC
   Webster M, 2008, CLIMATIC CHANGE, V89, P67, DOI 10.1007/s10584-008-9406-0
   Wigley TML, 2009, CLIMATIC CHANGE, V97, P85, DOI 10.1007/s10584-009-9585-3
   Yohe G, 2004, SCIENCE, V306, P416, DOI 10.1126/science.1101170
NR 28
TC 71
Z9 82
U1 1
U2 29
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0165-0009
EI 1573-1480
J9 CLIMATIC CHANGE
JI Clim. Change
PD JUN
PY 2012
VL 112
IS 3-4
BP 569
EP 583
DI 10.1007/s10584-011-0260-0
PG 15
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA 943FJ
UT WOS:000304105600002
DA 2026-06-14
ER

PT J
AU Prinn, R
   Paltsev, S
   Sokolov, A
   Sarofim, M
   Reilly, J
   Jacoby, H
AF Prinn, Ronald
   Paltsev, Sergey
   Sokolov, Andrei
   Sarofim, Marcus
   Reilly, John
   Jacoby, Henry
TI Scenarios with MIT integrated global systems model: significant global
   warming regardless of different approaches
SO CLIMATIC CHANGE
LA English
DT Article
ID CLIMATE; FEEDBACKS; POLICY
AB A wide variety of scenarios for future development have played significant roles in climate policy discussions. This paper presents projections of greenhouse gas (GHG) concentrations, sea level rise due to thermal expansion and glacial melt, oceanic acidity, and global mean temperature increases computed with the MIT Integrated Global Systems Model (IGSM) using scenarios for twenty-first century emissions developed by three different groups: intergovernmental (represented by the Intergovernmental Panel on Climate Change), government (represented by the U.S. government Climate Change Science Program) and industry (represented by Royal Dutch Shell plc). In all these scenarios the climate system undergoes substantial changes. By 2100, the CO2 concentration ranges from 470 to 1020 ppm compared to a 2000 level of 365 ppm, the CO2-equivalent concentration of all greenhouse gases ranges from 550 to 1780 ppm in comparison to a 2000 level of 415 ppm, oceanic acidity changes from a current pH of around 8 to a range from 7.63 to 7.91, in comparison to a pH change from a preindustrial level by 0.1 unit. The global mean temperature increases by 1.8 to 7.0A degrees C relative to 2000. Such increases will require considerable adaptation of many human systems and will leave some aspects of the earth's environment irreversibly changed. Thus, the remarkable aspect of these different approaches to scenario development is not the differences in detail and philosophy but rather the similar picture they paint of a world at risk from climate change even if there is substantial effort to reduce emissions.
C1 [Prinn, Ronald; Paltsev, Sergey; Sokolov, Andrei; Sarofim, Marcus; Reilly, John; Jacoby, Henry] MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT)
RP Paltsev, S (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM paltsev@mit.edu
RI Sokolov, Andrei/JJF-8545-2023
OI Prinn, Ronald/0000-0001-5925-3801; Sarofim, Marcus/0000-0001-7753-1676;
   Paltsev, Sergey/0000-0003-3287-0732
FU U.S. Department of Energy; U.S. Environmental Protection Agency; U.S.
   National Science Foundation; U.S. National Aeronautics and Space
   Administration; U.S. National Oceanographic and Atmospheric
   Administration; Industry and Foundation Sponsors of the MIT Joint
   Program on the Science and Policy of Global Change
FX We thank Martin Haigh, Stephanie Dutkiewicz, and Mort Webster for their
   help with the scenarios discussed here. The IGSM model used here is
   supported by the U.S. Department of Energy, U.S. Environmental
   Protection Agency, U.S. National Science Foundation, U.S. National
   Aeronautics and Space Administration, U.S. National Oceanographic and
   Atmospheric Administration and the Industry and Foundation Sponsors of
   the MIT Joint Program on the Science and Policy of Global Change.
CR [Anonymous], 180 MIT JOINT PROGR
   [Anonymous], 2005, MIT EMISSIONS PREDIC
   [Anonymous], SHELL EN SCEN 2050
   [Anonymous], 8 MIT JOINT PROGR SC
   [Anonymous], OC AC DU INCR ATM CA
   Clarke L.E., 2007, U.S. Climate Change Science Program Synthesis and Assessment Product 2.1a
   Doney SC, 2009, ANNU REV MAR SCI, V1, P169, DOI 10.1146/annurev.marine.010908.163834
   Forest CE, 2008, TELLUS A, V60, P911, DOI 10.1111/j.1600-0870.2008.00346.x
   [Houghton J.T. IPCC. IPCC.], 2001, CLIMATE CHANGE, DOI DOI 10.1002/QJ.200212858119
   Meehl GA, 2007, AR4 CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P747
   Moss R., [No title captured]
   Nakicenvoic N., 2000, Special report on emissions scenarios: A special report of working group iii of the intergovernmental panel on climate change
   Plattner GK, 2008, J CLIMATE, V21, P2721, DOI 10.1175/2007JCLI1905.1
   [Portner H.-O. IPCC IPCC], 2019, Climate change 2022-Impacts, adaptation and vulnerability, DOI [DOI 10.1017/9781009325844, DOI 10.1017/9781009325844.008]
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Sabine CL, 2004, SCOPE SER, V62, P17
   Sarofim MC, 2005, GLOBAL PLANET CHANGE, V47, P266, DOI 10.1016/j.gloplacha.2004.10.022
   Sokolov A., 2005, MIT integrated global system model (IGSM) version 2: model description and baseline evaluation
   Sokolov AP, 2009, J CLIMATE, V22, P5175, DOI 10.1175/2009JCLI2863.1
   Sokolov AP, 2008, J CLIMATE, V21, P3776, DOI 10.1175/2008JCLI2038.1
   Van Vuuren DP, 2008, P NATL ACAD SCI USA, V105, P15258, DOI 10.1073/pnas.0711129105
NR 21
TC 19
Z9 21
U1 0
U2 27
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0165-0009
EI 1573-1480
J9 CLIMATIC CHANGE
JI Clim. Change
PD FEB
PY 2011
VL 104
IS 3-4
BP 515
EP 537
DI 10.1007/s10584-009-9792-y
PG 23
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA 706FV
UT WOS:000286204300007
DA 2026-06-14
ER

PT J
AU Reilly, J
   Paltsev, S
   Felzer, B
   Wang, X
   Kicklighter, D
   Melillo, J
   Prinn, R
   Sarofim, M
   Sokolov, A
   Wang, C
AF Reilly, J.
   Paltsev, S.
   Felzer, B.
   Wang, X.
   Kicklighter, D.
   Melillo, J.
   Prinn, R.
   Sarofim, M.
   Sokolov, A.
   Wang, C.
TI Global economic effects of changes in crops, pasture, and forests due to
   changing climate, carbon dioxide, and ozone
SO ENERGY POLICY
LA English
DT Article
DE climate change; ozone damage; agriculture
ID CONTERMINOUS UNITED-STATES; LAND-USE; EMISSIONS; IMPACT; CO2;
   AGRICULTURE; MODEL; FOOD; SEQUESTRATION; DISTRIBUTIONS
AB Multiple environmental changes will have consequences for global vegetation. To the extent that crop yields and pasture and forest productivity are affected, there can be important economic consequences. We examine the combined effects of changes in climate, increases in carbon dioxide (CO2), and changes in tropospheric ozone on crop, pasture, and forest lands and the consequences for the global and regional economies. We examine scenarios where there is limited or little effort to control these substances, and policy scenarios that limit emissions of CO2 and ozone precursors. We find the effects of climate and CO2 to be generally positive, and the effects of ozone to be very detrimental. Unless ozone is strongly controlled, damage could offset CO2 and climate benefits. We find that resource allocation among sectors in the economy, and trade among countries, can strongly affect the estimate of economic effect in a country. (C) 2007 Elsevier Ltd. All rights reserved.
C1 MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   Marine Biol Lab, Ecosyst Ctr, Woods Hole, MA 02543 USA.
C3 Massachusetts Institute of Technology (MIT); Marine Biological
   Laboratory - Woods Hole
RP Reilly, J (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
EM jreilly@mit.edu
RI ; Felzer, Benjamin/AAB-3456-2021; Sokolov, Andrei/JJF-8545-2023
OI Prinn, Ronald/0000-0001-5925-3801; Felzer, Benjamin/0000-0002-3990-3739;
   Paltsev, Sergey/0000-0003-3287-0732; Sarofim, Marcus/0000-0001-7753-1676
CR ADAMS RM, 1986, AM J AGR ECON, V68, P886, DOI 10.2307/1242135
   ADAMS RM, 1990, NATURE, V345, P219, DOI 10.1038/345219a0
   Alig RJ, 2002, FOREST ECOL MANAG, V169, P3, DOI 10.1016/S0378-1127(02)00290-6
   Babiker M, 2004, ENERG J, V25, P33
   Babiker M., 2002, ENVIRON SCI POLICY, V5, P195, DOI [10.1016/S1462-9011(02)00035-7, DOI 10.1016/S1462-9011(02)00035-7]
   Babiker M. H., 2001, MIT JOINT PROGRAM SC
   Darwin R, 1996, ECOL ECON, V17, P157, DOI 10.1016/S0921-8009(96)80004-8
   Dimaranan B.V., 2002, The GTAP 5 Data Base
   Felzer B, 2005, CLIMATIC CHANGE, V73, P345, DOI 10.1007/s10584-005-6776-4
   Felzer B, 2004, TELLUS B, V56, P230, DOI 10.1111/j.1600-0889.2004.00097.x
   Forest CE, 2002, SCIENCE, V295, P113, DOI 10.1126/science.1064419
   Forest CE, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL023977
   GITAY H, 2001, CLIMATE CHANGE 2001, pCH5
   Izaurralde RC, 2003, AGR FOREST METEOROL, V117, P97, DOI 10.1016/S0168-1923(03)00024-8
   Jacoby HD, 1997, ENERG J, V18, P31
   Kicklighter DW, 1999, TELLUS B, V51, P343, DOI 10.1034/j.1600-0889.1999.00017.x
   Lawrence MG, 1999, J GEOPHYS RES-ATMOS, V104, P26245, DOI 10.1029/1999JD900425
   Long SP, 2006, SCIENCE, V312, P1918, DOI 10.1126/science.1114722
   Mahowald NM, 1997, J GEOPHYS RES-ATMOS, V102, P28139, DOI 10.1029/97JD02084
   MAYER M, 2000, MIT JOINT PROGRAM SC
   McFarland JR, 2004, ENERG ECON, V26, P685, DOI 10.1016/j.eneco.2004.04.026
   MELILLO JM, 1993, NATURE, V363, P234, DOI 10.1038/363234a0
   MENDELSOHN R, 1994, AM ECON REV, V84, P753
   Parry M, 1999, GLOBAL ENVIRON CHANG, V9, pS51, DOI 10.1016/S0959-3780(99)00018-7
   Parry ML, 2004, GLOBAL ENVIRON CHANG, V14, P53, DOI 10.1016/j.gloenvcha.2003.10.008
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Ramankutty N, 1999, GLOBAL BIOGEOCHEM CY, V13, P997, DOI 10.1029/1999GB900046
   Ramankutty N, 1998, GLOBAL BIOGEOCHEM CY, V12, P667, DOI 10.1029/98GB02512
   Rasch PJ, 1997, J GEOPHYS RES-ATMOS, V102, P28127, DOI 10.1029/97JD02087
   Reich PB, 1987, TREE PHYSIOL, V3, P63, DOI 10.1093/treephys/3.1.63
   REILLY J, 1993, AM ECON REV, V83, P306
   Reilly J, 1999, NATURE, V401, P549, DOI 10.1038/44069
   Reilly J, 2003, CLIMATIC CHANGE, V57, P43, DOI 10.1023/A:1022103315424
   Reilly J, 2002, ENVIRON MODEL ASSESS, V7, P217, DOI 10.1023/A:1020910820102
   Reilly JM, 1999, CLIMATIC CHANGE, V43, P745, DOI 10.1023/A:1005553518621
   ROSENZWEIG C, 1994, NATURE, V367, P133, DOI 10.1038/367133a0
   Tian H, 1999, TELLUS B, V51, P414, DOI 10.1034/j.1600-0889.1999.00021.x
   Tian HQ, 2003, GLOBAL PLANET CHANGE, V37, P201, DOI 10.1016/S0921-8181(02)00205-9
   TOBEY J, 1992, J AGR RESOUR ECON, V17, P195
   Wang XP, 2004, ATMOS ENVIRON, V38, P4383, DOI 10.1016/j.atmosenv.2004.03.067
   Webster MD, 2002, ATMOS ENVIRON, V36, P3659, DOI 10.1016/S1352-2310(02)00245-5
   WESTENBARGER DA, 1994, ATMOS ENVIRON, V28, P2895, DOI 10.1016/1352-2310(94)90338-7
   XIAO Y, 1997, J CIRCUITS SYSTEMS, V2, P49
NR 43
TC 77
Z9 90
U1 0
U2 59
PU ELSEVIER SCI LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
SN 0301-4215
EI 1873-6777
J9 ENERG POLICY
JI Energy Policy
PD NOV
PY 2007
VL 35
IS 11
BP 5370
EP 5383
DI 10.1016/j.enpol.2006.01.040
PG 14
WC Economics; Energy & Fuels; Environmental Sciences; Environmental Studies
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Business & Economics; Energy & Fuels; Environmental Sciences & Ecology
GA 225VC
UT WOS:000250545300011
OA Green Submitted
DA 2026-06-14
ER

PT C
AU Prinn, RG
AF Prinn, RG
BE Sparks, RSJ
   Hawkesworth, CJ
TI Complexities in the climate system and uncertainties in forecasts
SO STATE OF THE PLANET: FRONTIERS AND CHALLENGES IN GEOPHYSICS
SE Geophysical Monograph Book Series
LA English
DT Proceedings Paper
CT 23rd General Assembly of the International-Union-of-Geodesy-and
   Geophysics
CY JUL, 2003
CL Sapporo, JAPAN
SP Int Union Geodesy & Geophys
ID MODEL; FEEDBACKS; CHEMISTRY
AB The global atmosphere is a chemically complex and dynamic system, interacting both internally, mostly within the troposphere and stratosphere, and with the oceans, land, and living organisms. Its composition is changing today, and has also changed markedly over the last 400,000 yr. Current understanding of this complex system resulting from recent observations, theory, and laboratory and modeling studies is reviewed. Also, results are presented from the Integrated Global System Model (IGSM). This is a coupled model of economic development, atmospheric chemistry, climate dynamics and ecosystem processes that explores possible future changes in atmospheric composition and climate. The results of an uncertainty analysis involving hundreds of runs of the IGSM imply that, without specific mitigation policies, the global average surface temperature may rise between 1.0 and 4.9degreesC from 1990 to 2100 (95% confidence limits). Polar temperatures, absent policy, are projected to rise from about 1 to 12degreesC (95% limits) with obvious great risks for high latitude ecosystems and ice sheets at the high end of this range. Analysis of the Kyoto Protocol, and a more stringent climate mitigation policy, shows the difficulties in accounting simply for the effects of other greenhouse gases relative to carbon dioxide. Also, the greatest effect of these policies is to lower the probability of extreme changes as opposed to lowering the medians.
C1 MIT, Dept Earth Atmospher & Planetary Sci, Cambridge, MA 02139 USA.
C3 Massachusetts Institute of Technology (MIT)
RP Prinn, RG (corresponding author), MIT, Dept Earth Atmospher & Planetary Sci, 77 Massachusetts Ave, Cambridge, MA 02139 USA.
OI Prinn, Ronald/0000-0001-5925-3801
CR Allen M, 2001, SCIENCE, V293, P430
   [Anonymous], 2001, Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC)
   Ehhalt DH, 1999, T PHYS CHEM, V6, P21
   Forest CE, 2002, SCIENCE, V295, P113, DOI 10.1126/science.1064419
   Forest CE, 2001, CLIM DYNAM, V18, P277, DOI 10.1007/s003820100175
   JACOBY H, 2004, AGU MONOGRAPH
   Jones PD, 1999, REV GEOPHYS, V37, P173, DOI 10.1029/1999RG900002
   Kamenkovich IV, 2002, CLIM DYNAM, V19, P585, DOI 10.1007/s00382-002-0246-8
   Kamenkovich IV, 2003, CLIM DYNAM, V21, P119, DOI 10.1007/s00382-003-0325-5
   Mayer M, 2000, J GEOPHYS RES-ATMOS, V105, P22869, DOI 10.1029/2000JD900307
   [Portner H.-O. IPCC IPCC], 2019, Climate change 2022-Impacts, adaptation and vulnerability, DOI [DOI 10.1017/9781009325844, DOI 10.1017/9781009325844.008]
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   PRINN RG, 1994, AMBIO, V23, P50
   Prinn RG, 2003, ANNU REV ENV RESOUR, V28, P29, DOI 10.1146/annurev.energy.28.011503.163425
   Prinn RG, 2001, SCIENCE, V292, P1882, DOI 10.1126/science.1058673
   Reilly J, 1999, NATURE, V401, P549, DOI 10.1038/44069
   Reilly J, 2001, SCIENCE, V293, P430
   RIAL JA, 2004, IN PRESS CLIMATIC CH
   Schneider S.H., 1992, Climate System Modeling, P3
   Sokolov AP, 2003, J CLIMATE, V16, P1573, DOI 10.1175/1520-0442-16.10.1573
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   Watson R, 2001, CLIMATE CHANGE 2001: THE SCIENTIFIC BASIS, pIX
   Webster M, 2003, CLIMATIC CHANGE, V61, P295, DOI 10.1023/B:CLIM.0000004564.09961.9f
   Webster MD, 2002, ATMOS ENVIRON, V36, P3659, DOI 10.1016/S1352-2310(02)00245-5
   Xiao X, 1998, GLOBAL BIOGEOCHEM CY, V12, P345, DOI 10.1029/98GB01035
   2004, MIT JOINT PROGRAM SC
NR 27
TC 2
Z9 2
U1 0
U2 0
PU AMER GEOPHYSICAL UNION
PI WASHINGTON
PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA
SN 0065-8448
BN 0-87590-415-7
J9 GEOPHYS MONOGR SER
PY 2004
VL 150
BP 297
EP 305
DI 10.1029/150GM23
PG 9
WC Geochemistry & Geophysics
WE Conference Proceedings Citation Index - Science (CPCI-S)
SC Geochemistry & Geophysics
GA BBO48
UT WOS:000226692700022
DA 2026-06-14
ER

PT J
AU Webster, MD
   Babiker, M
   Mayer, M
   Reilly, JM
   Harnisch, J
   Hyman, R
   Sarofim, MC
   Wang, C
AF Webster, MD
   Babiker, M
   Mayer, M
   Reilly, JM
   Harnisch, J
   Hyman, R
   Sarofim, MC
   Wang, C
TI Uncertainty in emissions projections for climate models
SO ATMOSPHERIC ENVIRONMENT
LA English
DT Article
DE atmospheric chemistry; Monte Carlo simulation; air pollution; economic
   models; greenhouse gases; earth systems modeling
ID KYOTO PROTOCOL; SENSITIVITY; CHEMISTRY; LINKING
AB Future global climate projections are subject to large uncertainties, Major sources of this uncertainty are projections of anthropogenic emissions. We evaluate the uncertainty in future anthropogenic emissions using a computable general equilibrium model of the world economy. Results are simulated through 2100 for carbon dioxide (CO2) methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6), sulfur dioxide (SO2), black carbon (BC) and organic carbon (OC), nitrogen oxides (NOx), carbon monoxide (CO), ammonia (NH3) and non-methane volatile organic compounds (NMVOCs). We construct mean and upper and lower 95% emissions scenarios (available from the authors at 1degrees x 1degrees latitude-longitude grid). Using the MIT Integrated Global System Model (IGSM), we find a temperature change range in 2100 of 0.9 to 4.0degreesC, compared with the Intergovernmental Panel on Climate Change emissions scenarios that result in a range of 1.3 to 3.6degreesC when simulated through MIT IGSM. (C) 2002 Elsevier Science Ltd, All rights reserved.
C1 Univ N Carolina, Dept Publ Policy, Chapel Hill, NC 27599 USA.
   MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   AVL List GmbH, A-8020 Graz, Austria.
   ECOFYS Energy & Environ,, Energy Technol Ctr, D-90459 Nurnberg, Germany.
C3 University of North Carolina; University of North Carolina Chapel Hill;
   Massachusetts Institute of Technology (MIT); Anstalt fur
   Verbrennungskraftmaschinen List
RP Webster, MD (corresponding author), Univ N Carolina, Dept Publ Policy, CB 3435, Chapel Hill, NC 27599 USA.
RI ; /AAH-8057-2020
OI Sarofim, Marcus/0000-0001-7753-1676; 
CR [Anonymous], J GEOPHYS RES, DOI DOI 10.1029/97JD01654
   Babiker M, 2000, ENERG POLICY, V28, P525, DOI 10.1016/S0301-4215(00)00033-1
   Babiker M. H., 2001, 71 MIT
   FENHANN J, 2000, NONCO2 GREENHOUSE GA
   [Field C.B. IPCC. IPCC.], 2011, Workshop Report of the Intergovernmental Panel on Climate Change Workshop on Impacts of Ocean Acidification on Marine Biology and Ecosystems, DOI 10.1093/wentk/9780199996698.003.0009
   Hansen J, 2000, P NATL ACAD SCI USA, V97, P9875, DOI 10.1073/pnas.170278997
   Harnisch J, 2000, NON-CO2 GREENHOUSE GASES: SCIENTIFIC UNDERSTANDING, CONTROL AND IMPLEMENTATION, P231
   Mayer M, 2000, J GEOPHYS RES-ATMOS, V105, P22869, DOI 10.1029/2000JD900307
   MELILLO JM, 1993, NATURE, V363, P234, DOI 10.1038/363234a0
   MORGAN MG, 1990, UNCERTAINTY GUIDE DE
   MOSIER A, 1998, IGACTIVITIES
   Moss R.H., 2000, GUIDANCE PAPERS CROS, P33
   NORDHAUS WD, 1983, CHANGING CLIMATE
   OLIVIER JGJ, 1995, 771060002 RIVM
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Reilly J, 1999, NATURE, V401, P549, DOI 10.1038/44069
   Reilly J.M., 1987, Energy Journal, V8, P1, DOI [10.5547/ISSN0195-6574-EJ-Vol8-No3-1, DOI 10.5547/ISSN0195-6574-EJ-VOL8-NO3-1]
   Seinfeld J.H. Pandis., 2016, ATMOS CHEM PHYS, V3rd
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   *SRES, 2000, SPEC REP EM SCEN
   Tian H, 1999, TELLUS B, V51, P414, DOI 10.1034/j.1600-0889.1999.00021.x
   TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   Wang C., 1999, Chemosphere - Global Change Science, V1, P73, DOI DOI 10.1016/S1465-9972(99)00016-1
   Webster MD, 2000, CLIMATIC CHANGE, V46, P417, DOI 10.1023/A:1005685317358
   WEBSTER MD, 2001, 71 MIT
   Weyant JP, 1999, ENERG J, pVII
   Xiao X, 1997, TELLUS B, V49, P18, DOI 10.1034/j.1600-0889.49.issue1.2.x
   Xiao X, 1998, GLOBAL BIOGEOCHEM CY, V12, P345, DOI 10.1029/98GB01035
NR 29
TC 91
Z9 108
U1 1
U2 14
PU PERGAMON-ELSEVIER SCIENCE LTD
PI OXFORD
PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
SN 1352-2310
J9 ATMOS ENVIRON
JI Atmos. Environ.
PD AUG
PY 2002
VL 36
IS 22
BP 3659
EP 3670
AR PII S1352-2310(02)00245-5
DI 10.1016/S1352-2310(02)00245-5
PG 12
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA 584XR
UT WOS:000177494500008
DA 2026-06-14
ER

PT J
AU Reilly, J
   Prinn, R
   Harnisch, J
   Fitzmaurice, J
   Jacoby, H
   Kicklighter, D
   Melillo, J
   Stone, P
   Sokolov, A
   Wang, C
AF Reilly, J
   Prinn, R
   Harnisch, J
   Fitzmaurice, J
   Jacoby, H
   Kicklighter, D
   Melillo, J
   Stone, P
   Sokolov, A
   Wang, C
TI Multi-gas assessment of the Kyoto Protocol
SO NATURE
LA English
DT Article
ID CLIMATE-CHANGE; TERRESTRIAL; STABILIZATION; EMISSIONS; MODEL
AB The Kyoto Protocol allows reductions in emissions of several 'greenhouse' gases to be credited against a CO2-equivalent emissions limit, calculated using 'global warming potential' indices for each gas, Using an integrated global-systems model, it is shown that a multi-gas control strategy could greatly reduce the costs of fulfilling the Kyoto Protocol compared with a CO2-only strategy. Extending the Kyoto Protocol to 2100 without more severe emissions reductions shows little difference between the two strategies in climate and ecosystem effects. Under a more stringent emissions policy, the use of global warming potentials as applied in the Kyoto Protocol leads to considerably more mitigation of climate change for multi-gas strategies than for the-supposedly equivalent-CO2-only control, thus emphasizing the limits of global warming potentials as a tool for political decisions.
C1 MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA.
C3 Massachusetts Institute of Technology (MIT); Marine Biological
   Laboratory - Woods Hole
RP Reilly, J (corresponding author), MIT, Joint Program Sci & Policy Global Change, 77 Massachusetts Ave,Bldg E40, Cambridge, MA 02139 USA.
EM jreilly@mit.edu
RI Sokolov, Andrei/JJF-8545-2023
OI Prinn, Ronald/0000-0001-5925-3801
CR ADAMS RM, 1992, GLOBAL CHANGE EC ISS, P273
   [Anonymous], CLIMATE CHANGE 1995
   [Anonymous], 1999, CHAPTER 1 INTRO, P1
   Cao MK, 1998, NATURE, V393, P249, DOI 10.1038/30460
   COLE V, 1996, CLIMATE CHANGE 1995, P726
   Cook Elizabeth, 1995, Lifetime Commitments: Why Climate Policy-makers Cant Afford to Overlook Fully Fluorinated Compounds
   DAILY GC, 1997, DAILY SERVICES SOC D
   de Jager D, 1999, GREENHOUSE GAS CONTROL TECHNOLOGIES, P503, DOI 10.1016/B978-008043018-8/50081-3
   DENBALY M, 1993, AM J AGR EC, V75
   Eckaus R, 1992, ENERG J, V13, P25
   *EN INF ADM, DOEEIA048499
   Fan S, 1998, SCIENCE, V282, P442, DOI 10.1126/science.282.5388.442
   FERNANDEZCORNEJ.J, 1993, TB1816 USDA EC RES S
   FOLEY JA, 1994, NATURE, V371, P52, DOI 10.1038/371052a0
   Goulder LH, 1999, RESOUR ENERGY ECON, V21, P211, DOI 10.1016/S0928-7655(99)00004-4
   Harnisch J, 1999, EPD CONG, P797
   Harnisch J, 1999, ENVIRON SCI TECHNOL, V33, p56A, DOI 10.1021/es992650c
   Hoffert MI, 1998, NATURE, V395, P881, DOI 10.1038/27638
   JACOBY H, 1988, 43 MIT JOINT PROGR S
   Jacoby HD, 1997, ENERG J, V18, P31
   KRUGER D, 1999, INTEGRATED ASSESSMEN, P1
   Lind Robert C., 1998, EC POLICY ISSUES CLI, P59
   Maiss M, 1998, ENVIRON SCI TECHNOL, V32, P3077, DOI 10.1021/es9802807
   MCCULLOCH A, 1995, ENVIRON INT, V21, P353, DOI 10.1016/0160-4120(95)00038-M
   Melillo JM, 1999, SCIENCE, V283, P183, DOI 10.1126/science.283.5399.183
   NILSSON S, 1995, CLIMATIC CHANGE, V30, P267, DOI 10.1007/BF01091928
   NORDHAUS WD, 1994, MANAGING GLOBAL COMM, P49
   Oram DE, 1998, GEOPHYS RES LETT, V25, P35, DOI 10.1029/97GL03483
   Prinn R, 1999, CLIMATIC CHANGE, V41, P469, DOI 10.1023/A:1005326126726
   Reilly J., 1998, EC POLICY ISSUES CLI, P243
   REILLY J, 1999, 45 MIT JOINT PROGR S
   Reilly J.M., 1993, ENVIRON, V3, P41, DOI [10.1007/BF00338319, DOI 10.1007/BF00338319]
   RICHARDS KR, 1992, GLOBAL CHANGE EC ISS, P288
   Schimel D, 1995, CLIMATE CHANGE 1995: THE SCIENCE OF CLIMATE CHANGE, P65
   Schmalensee R., 1993, The Energy Journal, V14, P245, DOI [10.5547/ISSN0195-6574-EJ-Vol14-No1-10, DOI 10.5547/ISSN0195-6574-EJ-VOL14-NO1-10, 10.5547/issn0195-6574-ej-vol14-no1-10]
   SCHNEIDER SH, 1994, SCIENCE, V263, P341, DOI 10.1126/science.263.5145.341
   SMITH TM, 1993, NATURE, V361, P523, DOI 10.1038/361523a0
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   *US DOE, 1997, SCEN US CARB RED
   VICTOR DG, 1998, FUTURE EMISSIONS SUL
   VICTOR DG, IN PRESS CLIM CHANGE
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   WANG C, 1998, 35 MIT JOINT PROGR S
   WANG C, IN PRESS CHEMOSPHERE
   Wigley TML, 1996, NATURE, V379, P240, DOI 10.1038/379240a0
   Woodwell GM., 1995, Biotic Feedbacks in the Global Climatic System: Will the Warming Feed the Warming?, P393
   Xiao X, 1998, GLOBAL BIOGEOCHEM CY, V12, P345, DOI 10.1029/98GB01035
   YANG Z, 1996, 6 MIT JOINT PROGR SC
NR 48
TC 252
Z9 285
U1 1
U2 64
PU NATURE PORTFOLIO
PI BERLIN
PA HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
SN 0028-0836
EI 1476-4687
J9 NATURE
JI Nature
PD OCT 7
PY 1999
VL 401
IS 6753
BP 549
EP 555
DI 10.1038/44069
PG 7
WC Multidisciplinary Sciences
WE Science Citation Index Expanded (SCI-EXPANDED)
SC Science & Technology - Other Topics
GA 244MG
UT WOS:000083054900040
OA Green Submitted
DA 2026-06-14
ER

PT J
AU Prinn, R
   Jacoby, H
   Sokolov, A
   Wang, C
   Xiao, X
   Yang, Z
   Eckhaus, R
   Stone, P
   Ellerman, D
   Melillo, J
   Fitzmaurice, J
   Kicklighter, D
   Holian, G
   Liu, Y
AF Prinn, R
   Jacoby, H
   Sokolov, A
   Wang, C
   Xiao, X
   Yang, Z
   Eckhaus, R
   Stone, P
   Ellerman, D
   Melillo, J
   Fitzmaurice, J
   Kicklighter, D
   Holian, G
   Liu, Y
TI Integrated global system model for climate policy assessment: Feedbacks
   and sensitivity studies
SO CLIMATIC CHANGE
LA English
DT Review
ID OCEAN-ATMOSPHERE MODEL; NET PRIMARY PRODUCTION; GENERAL-CIRCULATION
   MODELS; INCREASING CARBON-DIOXIDE; NITROUS-OXIDE EVOLUTION;
   3-DIMENSIONAL MODEL; EQUILIBRIUM RESPONSES; TRANSIENT-RESPONSE; METHANE
   EMISSIONS; RAINFALL EVENTS
AB Alternative policies to address global climate change are being debated in many nations and within the United Nations Framework Convention on Climate Change. To help provide objective and comprehensive analyses in support of this process, we have developed a model of the global climate system consisting of coupled sub-models of economic growth and associated emissions, natural fluxes, atmospheric chemistry, climate, and natural terrestrial ecosystems. The framework of this Integrated Global System Model is described and the results of sample runs and a sensitivity analysis are presented. This multi-component model addresses most of the major anthropogenic and natural processes involved in climate change and also is computationally efficient. As such, it can be used effectively to study parametric and structural uncertainty and to analyze the costs and impacts of many policy alternatives.
   Initial runs of the model have helped to define and quantify a number of feedbacks among the sub-models, and to elucidate the geographical variations in several variables that are relevant to climate science and policy. The effect of changes in climate and atmospheric carbon dioxide levels on the uptake of carbon and emissions of methane and nitrous oxide by land ecosystems is one potentially important feedback which has been identified. The sensitivity analysis has enabled preliminary assessment of the effects of uncertainty in the economic, atmospheric chemistry, and climate sub-models as they influence critical model results such as predictions of temperature, sea level, rainfall, and ecosystem productivity. We conclude that uncertainty regarding economic growth, technological change, deep oceanic circulation, aerosol radiative forcing, and cloud processes are important influences on these outputs.
C1 MIT, Joint Program Sci & Policy Global Change, Cambridge, MA 02139 USA.
   Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA.
C3 Massachusetts Institute of Technology (MIT); Marine Biological
   Laboratory - Woods Hole
RP Prinn, R (corresponding author), MIT, Joint Program Sci & Policy Global Change, Bldg E40-271, Cambridge, MA 02139 USA.
RI Xiao, Xiangming/ABS-9360-2022; Sokolov, Andrei/JJF-8545-2023
OI Xiao, Xiangming/0000-0003-0956-7428; Prinn, Ronald/0000-0001-5925-3801; 
CR Alcamo J, 1996, GLOBAL ENVIRON CHANG, V6, P261, DOI 10.1016/S0959-3780(96)00026-X
   ALCAMO J, 1994, WATER AIR SOIL POLL, V76, P37, DOI 10.1007/BF00478336
   Alcamo J., 1994, IMAGE 2 0 INTEGRATED
   [Anonymous], 1996, CLIMATE CHANGE 1995, P572
   [Anonymous], INTEGRATIVE ASSESSME
   ARMINGTON PS, 1969, INT MONET FUND S PAP, V16, P159
   ATKINSON R, 1992, J PHYS CHEM REF DATA, V21, P1125, DOI 10.1063/1.555918
   BARTLETT KB, 1993, CHEMOSPHERE, V26, P261, DOI 10.1016/0045-6535(93)90427-7
   BERNSTEIN PM, 1997, WORLD EC IMPACTS US
   BOLIN B, 1981, SCOPE, V16, P390
   BRASSEUR G, 1992, CLIMATE SYSTEM MODEL, P491
   BREWER PG, 1986, CHANGING CARBON CYCL, P592
   Broecker W.S., 1982, TRACERS SEA
   BULATAO RA, 1990, WORLD BANK POPULATIO
   Burniaux J. M., 1992, OECD Economic Studies, P49
   Calbo J, 1998, J GEOPHYS RES-ATMOS, V103, P3437, DOI 10.1029/97JD02654
   CAO MK, 1995, GLOBAL BIOGEOCHEM CY, V9, P183, DOI 10.1029/94GB03231
   CESS RD, 1990, J GEOPHYS RES-ATMOS, V95, P16601, DOI 10.1029/JD095iD10p16601
   Cramer W.P., 1993, VEGETATION DYNAMICS, P191
   CUBASCH U, 1992, CLIM DYNAM, V8, P55, DOI 10.1007/BF00209163
   CUNNOLD DM, 1994, J GEOPHYS RES-ATMOS, V99, P1107, DOI 10.1029/93JD02715
   DEHAAN BJ, 1994, WATER AIR SOIL POLL, V76, P283, DOI 10.1007/BF00478343
   DeMore WB, 1994, JPL PUBLICATION
   DEVRIES HJM, 1994, WATER AIR SOIL POLL, V76, P79, DOI 10.1007/BF00478337
   EDMONDS J, 1995, ENERG POLICY, V23, P309, DOI 10.1016/0301-4215(95)90157-3
   EDMONDS J, 1995, MODELLING GLOBAL CHA
   EDMONDS J, 1994, P INT WORKSH INT ASS
   FUNG I, 1991, J GEOPHYS RES-ATMOS, V96, P13033, DOI 10.1029/91JD01247
   GLECKLER PJ, 1995, GEOPHYS RES LETT, V22, P791, DOI 10.1029/95GL00113
   GOLOMBEK A, 1986, J GEOPHYS RES-ATMOS, V91, P3985, DOI 10.1029/JD091iD03p03985
   GOLOMBEK A, 1993, J ATMOS CHEM, V16, P179, DOI 10.1007/BF00702787
   GOYET C, 1989, DEEP-SEA RES, V36, P1635, DOI 10.1016/0198-0149(89)90064-2
   GREGORY JM, 1993, J CLIMATE, V6, P2247, DOI 10.1175/1520-0442(1993)006<2247:SLCUIA>2.0.CO;2
   HAHN J, 1988, CLIMATOLOGICAL DATA
   HANSEN J, 1993, RES EXPLOR, V9, P142
   Hansen J, 1997, J GEOPHYS RES-ATMOS, V102, P6831, DOI 10.1029/96JD03436
   HANSEN J, 1988, J GEOPHYS RES-ATMOS, V93, P9341, DOI 10.1029/JD093iD08p09341
   HANSEN J, 1983, MON WEATHER REV, V111, P609, DOI 10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2
   Hansen J., 1984, AGU GEOPHYSICAL MONO, V5, P130, DOI [DOI 10.1029/GM029P0130, 10.1029/gm029p0130]
   HASSELMANN K, 1993, CLIM DYNAM, V9, P53, DOI 10.1007/BF00210008
   Hindmarsh A.C., 1983, IMACS T SCI COMPUTAT, V1, P55, DOI DOI 10.12691/AJMO-4-1-1
   Houghton J.T., 1990, Climate Change: The IPCC Scientific Assessment, P365
   HULME M, 1995, ENERG POLICY, V23, P347, DOI 10.1016/0301-4215(95)90159-5
   *IPCC, 1992, CLIM CHANG 1992 SUPP, P200
   *IPCC, 1996, CLIM CHANG 1995 EC S, P439
   *IPCC, 1994, CLIM CHANG 1994 RAD, P339
   Jacoby HD, 1997, ENERG J, V18, P31
   JACOBY HD, 1997, CRITICAL ISSUES EC C, P225
   JONAS M, 1996, CLIMATIC CHANGE, V34, P469
   KELLER M, 1994, ENVIR SCI R, V48, P103
   KREILEMAN GJJ, 1994, WATER AIR SOIL POLL, V76, P231, DOI 10.1007/BF00478341
   LEEMANS R, 1991, RR9118 INT I APPL SY, P60
   Levitus S., 1982, NOAA PROF PAP, V13, P173, DOI DOI 10.1029/EO064I049P00962-02
   Li CS, 1996, GLOBAL BIOGEOCHEM CY, V10, P297, DOI 10.1029/96GB00470
   LI CS, 1992, J GEOPHYS RES-ATMOS, V97, P9759, DOI 10.1029/92JD00509
   Liss PS., 1986, ROLE AIR GAS EXCHANG, P113
   LIU L, 1994, THESIS MIT
   LIU Y, 1995, WMO IGAC C MEAS ASS, P205
   LIU Y, 1996, 10 MIT JOINT PROGR S, P219
   MAIERREIMER E, 1993, J PHYS OCEANOGR, V23, P731, DOI 10.1175/1520-0485(1993)023<0731:MCOTHL>2.0.CO;2
   MANABE S, 1991, J CLIMATE, V4, P785, DOI 10.1175/1520-0442(1991)004<0785:TROACO>2.0.CO;2
   MANNE AS, 1994, BUYING GREENHOUSE IN
   MAROTZKE J, 1995, J PHYS OCEANOGR, V25, P1350, DOI 10.1175/1520-0485(1995)025<1350:ATTTCA>2.0.CO;2
   MATSUOKA Y, 1995, ENERG POLICY, V23, P357, DOI 10.1016/0301-4215(95)90160-9
   Matthews E, 1987, GLOBAL BIOGEOCHEM CY, V1, P61, DOI 10.1029/GB001i001p00061
   McGuire AD, 1992, GLOBAL BIOGEOCHEM CY, V6, P101, DOI 10.1029/92GB00219
   MCGUIRE AD, 1993, CLIMATIC CHANGE, V24, P287, DOI 10.1007/BF01091852
   McGuire AD, 1997, GLOBAL BIOGEOCHEM CY, V11, P173, DOI 10.1029/97GB00059
   McGuire AD, 1995, J BIOGEOGR, V22, P785, DOI 10.2307/2845980
   MCKIBBIN WJ, 1993, COSTS IMPACTS BENEFI
   Meehl GA, 1996, GEOPHYS RES LETT, V23, P3755, DOI 10.1029/96GL03478
   MELILLO JM, 1995, GLOBAL BIOGEOCHEM CY, V9, P407
   MELILLO JM, 1993, NATURE, V363, P234, DOI 10.1038/363234a0
   Melillo JM, 1995, DAHL WS ENV, V16, P175
   MELILLO JM, 1994, CHANGES LAND USE LAN, P387
   MURPHY JM, 1995, J CLIMATE, V8, P57, DOI 10.1175/1520-0442(1995)008<0057:TROTHC>2.0.CO;2
   MURPHY JM, 1995, J CLIMATE, V8, P36, DOI 10.1175/1520-0442(1995)008<0036:TROTHC>2.0.CO;2
   *NCAR NAVY, 1984, GLOB 10 MIN EL DAT
   *OECD, 1993, TECHN REF MAN
   *OECD, 1993, GREEN US MAN
   OESCHGER H, 1975, TELLUS, V27, P168, DOI 10.1111/j.2153-3490.1975.tb01671.x
   OJIMA D, 1992, MODELING EARTH SYSTE, P488
   Oort A.H., 1983, GLOBAL ATMOSPHERIC C, P180
   Pan Y, 1996, GLOBAL CHANGE BIOL, V2, P5, DOI 10.1111/j.1365-2486.1996.tb00045.x
   PANDIS SN, 1989, J GEOPHYS RES-ATMOS, V94, P1105, DOI 10.1029/JD094iD01p01105
   Peixoto J.P. e., 1992, Physics of climate, P520
   PENG T-H, 1987, Tellus Series B Chemical and Physical Meteorology, V39, P439, DOI 10.1111/j.1600-0889.1987.tb00205.x
   POTTER CS, 1993, GLOBAL BIOGEOCHEM CY, V7, P811, DOI 10.1029/93GB02725
   PRINN R, 1990, J GEOPHYS RES-ATMOS, V95, P18369, DOI 10.1029/JD095iD11p18369
   Prinn R., 1992, MODELING EARTH SYSTE, P9
   PRINN RG, 1995, SCIENCE, V269, P187, DOI 10.1126/science.269.5221.187
   RAICH JW, 1991, ECOL APPL, V1, P399, DOI 10.2307/1941899
   RAMANATHAN V, 1989, SCIENCE, V243, P57, DOI 10.1126/science.243.4887.57
   REEBURGH WS, 1993, GLOBAL ATMOSPHERIC B, P165
   RUTHERFORD RF, 1994, GAMS MPSGE GAMS MILE
   Sarmiento JL, 1996, SCIENCE, V274, P1346, DOI 10.1126/science.274.5291.1346
   SARMIENTO JL, 1992, J GEOPHYS RES-OCEANS, V97, P3621, DOI 10.1029/91JC02849
   SCHIFFER RA, 1985, B AM METEOROL SOC, V66, P1498, DOI 10.1175/1520-0477(1985)066<1498:IGRDSA>2.0.CO;2
   SCHNEIDER DJ, 1992, CORONARY ARTERY DIS, V3, P26, DOI 10.1097/00019501-199201000-00004
   SENIOR CA, 1993, J CLIMATE, V6, P393, DOI 10.1175/1520-0442(1993)006<0393:CDACTI>2.0.CO;2
   SIEGENTHALER U, 1993, NATURE, V365, P119, DOI 10.1038/365119a0
   SIEGENTHALER U, 1992, TELLUS B, V44, P186, DOI 10.1034/j.1600-0889.1992.t01-2-00003.x
   Sokolov AP, 1998, CLIM DYNAM, V14, P291, DOI 10.1007/s003820050224
   SOKOLOV AP, 1997, UNPUB GEOPHYS RES LE
   SOKOLOV AP, 1995, 2 MIT JOINT PROGR SC, P46
   SOKOLOV AP, 1997, RES ACTIVITIES ATMOS
   STOCKER TF, 1994, TELLUS B, V46, P103, DOI 10.1034/j.1600-0889.1994.t01-1-00003.x
   STONE PH, 1990, J CLIMATE, V3, P726, DOI 10.1175/1520-0442(1990)003<0726:DOATDZ>2.0.CO;2
   STONE PH, 1987, J ATMOS SCI, V44, P3769, DOI 10.1175/1520-0469(1987)044<3769:DOATDZ>2.0.CO;2
   Takahashi T., 1980, ISOTOPE MARINE CHEM, P291
   TANS PP, 1990, SCIENCE, V247, P1431, DOI 10.1126/science.247.4949.1431
   Vörösmarty CJ, 1989, GLOBAL BIOGEOCHEM CY, V3, P241, DOI 10.1029/GB003i003p00241
   WANG C, 1995, J GEOPHYS RES-ATMOS, V100, P11357, DOI 10.1029/95JD00697
   Wang C, 1998, J GEOPHYS RES-ATMOS, V103, P3399, DOI 10.1029/97JD03465
   WANG C, 1995, WMO IGAC C MEAS ASS, P182
   WANG C, 1993, J GEOPHYS RES, V98, P74
   Washington W.M., 1989, CLIM DYNAM, V4, P1, DOI 10.1007/BF00195851
   WASHINGTON WM, 1993, CLIM DYNAM, V8, P123
   WETHERALD RT, 1988, J ATMOS SCI, V45, P1397, DOI 10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2
   WHITTAKER RH, 1973, HUM ECOL, V1, P357, DOI 10.1007/BF01536732
   Wigley T.M. L., 1993, CLIMATE SEA LEVEL CH, P111
   Xiao X, 1997, TELLUS B, V49, P18, DOI 10.1034/j.1600-0889.49.issue1.2.x
   XIAO X, 1995, 3 MIT JOINT PROGR SC, P20
   XIAO X, 1996, 8 MIT JOINT PROGR SC, P34
   XIAO X, 1996, 12 MIT JOINT PROGR S, P26
   Yang BX, 1996, T NONFERR METAL SOC, V6, P49
   YAO MS, 1987, J ATMOS SCI, V44, P65, DOI 10.1175/1520-0469(1987)044<0065:DOATDZ>2.0.CO;2
NR 127
TC 106
Z9 123
U1 1
U2 30
PU SPRINGER
PI DORDRECHT
PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
SN 0165-0009
EI 1573-1480
J9 CLIMATIC CHANGE
JI Clim. Change
PD MAR
PY 1999
VL 41
IS 3-4
BP 469
EP 546
DI 10.1023/A:1005326126726
PG 78
WC Environmental Sciences; Meteorology & Atmospheric Sciences
WE Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)
SC Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
GA 194JM
UT WOS:000080189100012
DA 2026-06-14
ER

EF